Mit Big Data and Social Analytics Certificate Review
Data scientist is one of the hottest jobs in the Information technology industry today. Data trends from Glassdoor clearly indicate that information technology is the best job anyone can get. The earth needs 5 meg data scientific discipline professionals this year. With the exponential corporeality of data existence produced and captured, it is logical to say that the demand for data analytics is just going to increase. More than and more companies are going to go on to hire information scientists to find meaningful insights and develop business strategies.
A certification in data science can go a long way to boost employability. Whether you are a student looking for a sparkling start to your career or a professional looking to expand your employment opportunities, data scientific discipline courses will assistance y'all develop the necessary skills that the recruiters are looking for.
Here is our list of Best Data Science Certifications, Courses & Programs for 2022 from accredited institutions. These include both free resources and paid data science certificate programs that are delivered online, are widely recognised and take benefited thousands of students and professionals.
i. Professional Certificate in Data Science from Harvard University (edX)
This Online Data Scientific discipline Document Program is offered by Harvard Academy through leading due east-learning platform edX. It prepares y'all with fundamental data science skills like R programming, auto learning and others using real globe example studies to requite you a jumpstart in roles of a data scientist.
This is a very reputable and intensive ii to 4 months long self-paced program. Information technology includes ix graduate-level courses that are taught by Harvard'south Professor of Biostatistics Rafael Irizarry and offered entirely online at a fraction of cost of traditional college, making it very accessible, affordable and flexible. The 9 courses that make up this data scientific discipline plan include R Basics, Visualization, Probability, Inference and Modeling, Productivity Tools, Wrangling, Linear Regression, Machine Learning and a Capstone project. Thus the courses brainstorm with basic fundamentals and progress to culminate with a Capstone project where you utilize the skills and noesis acquired throughout the course series to a existent world trouble. By the end of the programme you learn how to independently piece of work on a data analysis project.
Upon completion students receive a Professional Certificate in Data Scientific discipline that they tin highlight to their potential employers.
Central Highlights
- Foundational R programming skills (a required skill in over 65% data science jobs)
- Learn Statistical concepts such every bit probability, Statistical tools such as inference and modeling and how to utilise them in practise
- Gain feel with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
- Learn how to utilize R to implement linear regression
- Go familiar with essential productivity tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
- Implement car learning algorithms
- Learn fundamental data science concepts through motivating existent-globe example studies
Elapsing: ix courses, 2 to eight weeks per course, 102 to 184 hours of total effort
Rating: 4.9
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2. Data Science Specialization from Johns Hopkins University (Coursera)
This Information Science Specialization is a ten-course introduction to concepts and tools that you'll demand throughout the data science pipeline and is taught by renowned professors of Johns Hopkins University on Coursera platform. It aims to develop capability of learners to ask the right kind of questions, manipulate data sets, make inferences and create visualizations to publish results.
There are ten courses in this certification program with a Capstone project at the terminate. These courses comprehend tools that information analysts and data scientists work with like version control, markdown, git, GitHub, and RStudio, R Programming, Getting and Cleaning Data, Exploratory Data Assay techniques for summarizing data, Reproducible Research, Statistical Inference, Regression Models, Machine Learning, Developing Data Products. The Capstone Project will be drawn from real-world problems and conducted with government, industry or academic partners. It volition give the students an opportunity to demonstrate their data science skills to potential employers.
Beginner level feel in Python and some familiarity with Regression are listed every bit requirements for this course.
Key Highlights
- Use R to make clean, clarify, and visualize information
- Navigate the entire data science pipeline from data conquering to publication
- Apply GitHub to manage data science projects
- Perform regression analysis, least squares and inference using regression models
- Residuum both the theory and practise of practical mathematics to analyze and handle large-scale data sets
- Create models using formal techniques and methodologies of abstraction that can be automated to solve existent-globe bug
Elapsing: ten courses, eight months, 5 hours per week
Rating: 4.5
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three. IBM Data Scientific discipline Professional Document (Coursera)
This Data Science Certification Program has been developed past IBM and delivered through Coursera platform to help students or professionals get ready to pursue roles in information science. You lot will learn concepts of data science and machine learning with thorough hands-on and practical learning.
This is ane of the best Data Science Programs and comprises of ix courses that embrace post-obit information science topics in particular – fundamentals of data scientific discipline, open source tools and libraries, data science methodology, Python programming, working knowledge of databases and SQL, data analysis and visualization with Python, basics of machine learning followed past applied information scientific discipline capstone project to aid you consolidate your learning and apply skills learned to a real life project.
Each of the 9 courses typically contains 3 to 6 modules that need an average effort of ii-four hours per module. A complete beginner would take upwardly to 2-3 months to complete the program. Upon completion of this data science training, you lot are awarded a Document and IBM open bluecoat (in fact nine IBM badges for each of the nine courses included in the program) that demonstrate efficiency in information science.
Key Highlights
- Larn open source tools used in data science like Jupyter Notebooks, Zepplin, RStudio, and IBM Watson.
- Learn the nuts of Python, Pandas, and NumPy
- Build databases, collect and analyze information from them using Python
- Apply Python libraries to generate data visualizations
- Well designed content and all the topics are covered elaborately
- Graded Assignments with Peer Feedback
- Assignments and projects that provide practical skills with applicability to real jobs that employers value – random album generator, predict housing prices, best classifier model, boxing of neighbourhoods
- No prior programming or estimator scientific discipline knowledge is required
Duration: 9 courses, approx 2 months, 12 hours per week
Rating: 4.7
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iv. MicroMasters Program in Statistics and Data Science from MIT (edX)
This is a stand-lonely Data Scientific discipline and Statistics Certification plan designed by the MIT Establish for Data, Systems, and Society (IDSS) and delivered past edX. The goal of this Micromasters information science program is to chief the foundations of information science, statistics and machine learning.
It is one of the top information science programs and comprises of four intensive online courses followed by a well-nigh proctored online test to earn a certificate. These graduate–level courses include Probability, Data Analysis in Social Science, Fundamentals of Statistics, Car Learning with Python, Capstone Exam in Data Science and Statistics. The Probability class offered in this plan is essentially same equally the introduction to probability course taught on MIT campus and refined for 50 years. All the courses are taught by MIT kinesthesia with loftier quality and hands-on learning approach. It is suggested that you take grasp of single and multi-variable calculus and linear algebra, equally well as mathematical reasoning and Python programming to take upward the program.
Each form in this MIT Data Scientific discipline Document program runs for between 13 to sixteen weeks and one is expected to spend approximately 12-14 hours per calendar week on each course. Learners earn an individual Verified Certificate for each grade that they pass and learners who laissez passer the capstone exam at the end of the program receive a MicroMasters Program Credential.
Key Highlights
- Learn Information analysis techniques, car learning algorithms and apply them to existent world data sets
- In-depth introduction to the field of car learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects
- Acquire to clarify large data and brand data-driven predictions through probabilistic modeling and statistical inference and apply appropriate methodologies for extraction of meaningful data to assistance decision making.
- Develop and Build motorcar learning algorithms to make sense of the unstructured data and gain relevant insights into it.
- Piece of work on popular unsupervised learning methods such every bit clustering methodologies and supervised methods such as deep neural networks.
- Learners who successfully consummate this MIT MicroMasters credential tin apply to the MIT Doctoral Program in Social and Engineering Systems (SES) offered through the MIT IDSS and have this coursework recognized for credit.
- Learners can utilize for diverse job titles after the completion of this certification such as Data Scientist, Data Annotator, Systems Analyst, Business Intelligence Analyst, Data Engineer etc.
Duration: 5 courses, ii to 16 weeks per course, 12 to 14 hours per week
Rating: iv.six
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5. Applied Data Scientific discipline with Python Specialization by Academy of Michigan (Coursera)
This Coursera Data Scientific discipline program has been developed by 4 professors of University of Michigan. It aims to enable learners with a basic understanding of programming to effectively dispense and gain insight into information. Information technology comprises of five courses that delve into data science methods, techniques and skills using Python programming language. Information technology is expected that learners accept a basic working knowledge of Python or at least other programming background. This plan focuses on the application of statistical analysis, automobile learning, data visualization, text analysis and social network analysis. It teaches popular python toolkits such every bit pandas, matplotlib, scikit-acquire, nltk, and networkx to gain insight into data. Specifically the v courses are – Introduction to Data science in Python, Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python and Applied Social Network Assay in Python. Learners need to complete all five courses to earn the specialization certificate.
Key Highlights
- Analyze the connectivity of a social network
- Conduct an inferential statistical assay
- Learn Visualization basics with a focus on reporting and charting using the matplotlib library
- Discern whether a data visualization is good or bad and Develop all-time practices for creating basic visualizations and charts
- Enhance a data analysis with applied motorcar learning
- Learn Applied data mining such as clustering and classification
- Learn to take tabular information, clean information technology, dispense it, and run basic inferential statistical analysis on it
- Acquire models of network generation and the link prediction problem
Duration: 5 courses, 5 months, 7 hours per week
Rating: 4.vi
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vi. Deep Learning Specialization (Coursera)
Deep Learning and Machine Learning skills are highly in need. If you want to master them and build a career in AI, this Deep Learning Specialization course by deeplearning.ai is your all-time bet. Andrew Ng (CEO/Founder Landing AI; Co-founder, Coursera; Offshoot Professor, Stanford University; formerly Master Scientist, Baidu and founding lead of Google Brain), a very reputed and respected name in AI industry has developed this plan along with 2 professors of Standard university. This is one of the most sought subsequently programs on deep learning.
Delivered as five courses, this data science specialization plan teaches foundations of Deep Learning, how to build neural networks, and how to pb successful machine learning projects. Information technology is a bottom-up approach to learning neural networks — powerful non-linearity learning algorithms, at a beginner-mid level. You volition learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. The 5 courses are namely, Neural Networks and Deep Learning, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Structuring Automobile Learning Projects, Convolutional Neural Networks and Sequence Models.
The course curriculum has been very carefully designed with neatly timed videos and has a well-regulated pace. You need to have a basic knowledge of mathematics and machine learning and some programming background to take the course. Some feel in Python is an added reward equally the course is delivered using Python language.
Central Highlights
- Understanding of how neural networks work, along with How and Why We Make Them Deep
- Learn to Exist able to build, railroad train and apply fully connected deep neural networks
- Acquire TensorFlow and variety of optimization algorithms
- Work on case studies from healthcare, democratic driving, sign language reading, music generation, and natural language processing
- Interviews and Personal stories of heroes and meridian leaders in Deep Learning
Duration: 5 courses, three months, 11 hours per week
Rating: 4.nine
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7. Machine Learning Certification past Stanford University (Coursera)
This Machine Learning Certification Form has been developed past world renowned AI practiced Andrew Ng and provides details into nearly effective machine learning techniques and their implementation in real world. You not only learn the theory of machine learning and statistical pattern recognition but besides gain the practical cognition to quickly and powerfully utilise these techniques to solve new issues. This class is recognised as one of the best data science courses bachelor online.
Following topics are covered in this course – supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. Learners should have a basic knowledge of computer science principles and be familiar with basic linear algebra and basic probability theory.
The data scientific discipline machine learning course is highly involving with multiple videos in each lecture, followed past quizzes and assignments. Information technology is approximated that one would need xi weeks to take the grade spending effectually 5-seven hours a calendar week.
Key Highlights
- Work with large datasets from various fields and in different formats
- Understand parametric and non-parametric algorithms, clustering (k-Means algorithm), dimensionality reduction, anomaly detection among other of import topics
- Programming assignments designed to assistance empathize how to implement the learning algorithms in practice
- Acquire nigh Silicon Valley'due south best practices in innovation as it pertains to machine learning and AI
- Numerous example studies and applications to learn how to apply machine learning algorithms to edifice smart robots (perception, control), text agreement (web search, anti-spam), calculator vision, medical informatics, audio, database mining, and other areas
Duration: 55 hours
Rating: four.9
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8. Data Scientific discipline MicroMasters Certification past University of California, San Diego (edX)
This MicroMasters program is a series of graduate level courses in data science, designed past professors of University of California, San Diego and delivered online via edX. It is a very immersive program that can help to gain critical skills needed to accelerate equally a data scientist. Information technology aims to develop an in-depth understanding of the mathematical and computational tools that class the footing of data science and usage of those tools to make data-driven business decisions.
This UCSD Data Scientific discipline certification program very finer encompasses ii sides of data learning – the mathematical and the practical in the class of 4 courses. These courses are – Python for Data Scientific discipline, Probability and Statistics in Data Science using Python, Machine Learning Fundamentals and Large Data Analytics using Spark. Learners are introduced to a collection of powerful, open-source, tools needed to clarify data and to conduct data science. Specifically, they learn how to use:
- Python
- Jupyter notebook environment
- Numpy
- Matplotlib
- Git
- Pandas
- Scipy
- Apache Spark
At each stage of completing a course learners earn a verified certificate for the course. Afterwards completing all four program courses, they earn the MicroMasters Program Certificate.
Key Highlights
- Learn to load and make clean existent world data
- Learn to analyse big data using popular open source software to perform large-scale data analysis and present your findings in a convincing, visual style
- Learn to make reliable statistical inferences from noisy data
- Employ car learning to learn models for data
- Visualize complex data using tools covered in the lectures
- Use Apache Spark to analyze information that does not fit within the memory of a single computer
- Work on practical assignments and projects to enhance your portfolio and utilise the knowledge covered in the courses
- Acquire to build data science tools, explore public datasets, and discuss evidence-based findings
Duration: 4 courses, ten to xv weeks per form, 8 to 10 hours per week
Rating: 4.6
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nine. The Data Scientific discipline Course 2020: Complete Data Science Bootcamp (Udemy)
The Complete Data Science Bootcamp program from Udemy provides the entire toolbox y'all need to become a data scientist. Information technology progressively takes yous from basics of mathematics and statistics to advanced statistics, machine learning and tableau and more. This course includes 27 hours of on-demand video, 88 articles, 144 downloadable resources and total lifetime access.
This Udemy data science course is the one of the well-nigh effective, fourth dimension-efficient, and structured data scientific discipline grooming available online. Information technology covers following topics in detail – Basics of Data science, Mathematics (Calculus and Linear Algebra), Statistics, Python programming with NumPy, Pandas, Matplotlib and Seaborn, Tableau, Advanced Statistical Assay, and Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow. It includes wide multifariousness of animations, quizzes, exercises and bonus materials. One does not need whatsoever prior experience to take upwards this form, everything is taught from the scratch with each topic building on the previous ones and so you are prepped to piece of work as a information scientist, handle existent-life business organization cases and tin can take upwards more advanced specializations.
Cardinal Highlights
- Understand the mathematics behind Machine Learning
- Perform linear and logistic regressions in Python
- Exist able to create Motorcar Learning algorithms in Python, using NumPy, statsmodels and scikit-larn
- Employ state-of-the-fine art Deep Learning frameworks such as Google's TensorFlowDevelop while coding and solving tasks with large data
- Improve Car Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could meliorate functioning
- Learn how to pre-process data
- Carry out cluster and factor analysis
- Unfold the power of deep neural networks
- Utilise your skills to real-life business cases
Duration: 27 hours on-demand video, 88 manufactures, 144 downloadable resources
Rating: iv.5
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ten. Machine Learning A-Z™: Hands-On Python & R In Data Scientific discipline (Udemy)
Automobile Learning is a very broad subject area and condign an expert in this field can be very challenging. This Data Science Machine Learning grade on Udemy provides a clear pathway into the world of motorcar learning so participants can learn complex theory, algorithms and coding libraries in a simple and effective style. The form provides educational activity in both Python and R programming languages, which is very distinguishing in itself. It has around 100,000 5-star ratings and more than 665,000 students enrolled making it the about popular Udemy Data Science course.
The course is very detailed and dives deep into all aspects of motorcar learning with over 44 hours of video content spread across 290 lectures. It covers Regression, Nomenclature, Clustering, Clan Dominion Learning, Reinforcement Learning, Natural Language Processing, Deep Learning, Dimensionality Reduction. For each of these branches of motorcar learning, the course discusses between two-7 different algorithms and shows how to create and lawmaking each one of them in Python and R. There are besides takeaway templates included (in both Python and R) that students can download and use on their ain projects. Students have the selection of going with either Python or R (and skip the other) or effort out both languages to truly principal their automobile learning skills.
The grade takes an applied approach and is lighter math-wise. It is packed with practical exercises that are based on real-life examples, so autonomously from learning theory students get easily-on practise building their ain models. There are quizzes and homework challenges too. Additionally students are expected to post solutions to exercises via Q&A or PM to permit discussion and feedback by instructors and fellow students.
The course has been created by two professional person data scientists Kirill Eremenko and Hadelin de Ponteves, both of whom accept years of real earth data scientific discipline experience under their belt. They bring both bookish knowledge and real-life experience to the students and are known for their power to brand complex topics elementary and like shooting fish in a barrel to grasp.
Key Highlights
- Master the unabridged Auto Learning workflow in Python & R
- Larn an impressive number of powerful Machine Learning models and know how to combine them to solve whatsoever problem
- Understand how to make accurate predictions and do detailed analysis
- Know which Machine Learning model to choose for each type of problem
- Go access to comprehensive Q&A department that addresses most of the ordinarily encountered issues
- Course is constantly updated and new materials added
Duration : 44 hours on-demand video
Rating : four.v
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11. Data Science Nanodegree Courses (Udacity)
Udacity offers world-class Nanodegree programs in its School of Data Science. No affair what the skills and experience level of an individual, these programs offer a signal of entry into the world of Data. Whether 1 wants to primary data science programming with Python, R and SQL or become a data annotator or acquire business concern analytics, there is a program on offer to build the relevant skills.
The Nanodegree programs in the Udacity's School of Data Scientific discipline are organized around three principal roles: Business Analyst, Data Annotator and Data Scientist. They prepare learners for these roles based on their career goals, skills and feel levels.
Following are some of the Nanodegree courses in the Data Science field:
- Programming for Data Science with R – Learn the programming fundamentals required for a career in data science – R, SQL, Control Line, and Git.
- Information Scientist – Covers machine Learning & deep learning. Through projects designed by industry experts, students learn to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the deject.
- Information Visualization – Covers data visualization, tableau, dashboards etc. Students learn to combine data, visuals, and narrative to tell impactful stories and make data-driven decisions.
- Data Analyst – Covers Data Wrangling, Matplotlib, Bootstrapping, Pandas & NumPy, Statistics. Students larn to employ Python, SQL, and statistics to uncover insights, communicate disquisitional findings, and create data-driven solutions.
- Programming for Data Science with Python – Covers Python, Numpy & Pandas, SQL, Git & GitHub
- Information Engineer – Covers Data Modeling, Data Pipelines, Data Lakes, Spark, Airflow
- SQL – Covers SQL, PostgreSQL, JOINs, Subqueries, Window Functions, Partitions, Data Cleaning, DDL, DML, Relational and Not-Relational Databases
Udacity has partnered with industry leaders similar Tableau, Kaggle, and IBM Watson, to ensure these programs include in-demand skills that industry recruiters look for. They also provide personalized career services to the students like coaching sessions, interview prep communication, resume and profile review etc.
Fundamental Highlights
- Powerful Information Scientific discipline programs to jumpstart your career
- Build expertise in data manipulation, visualization, predictive analytics, machine learning, and data science
- Benefit from personalized mentorship, real-world projects and adept instruction
- Get Practical tips and knowhow of industry best practices
Duration : Self-Paced
Rating : 4.half dozen
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12. Python for Data Science and Machine Learning Bootcamp (Udemy)
This Python Data Scientific discipline course on Udemy is taught past Jose Portilla and is amongst the nigh sought after courses in data science and machine learning fields. It has more than 365,000 students enrolled and enjoys very high positive ratings. Information technology is a highly immersive form with over 25 hours of video content that takes students through a Python Crash course followed past information analysis and data visualization and machine learning algorithms. This grade has the most in-depth coverage of popular Python Data Scientific discipline libraries like NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn and more.
The course is structured very well. It starts with a crash course in Python (which acts a refresher on important syntaxes and topics) so moves to information analysis and information visualization using Python libraries. Lastly the form covers how to utilize Python in Automobile Learning. Information technology uses Jupyter Notebooks for the code written and executed.
The grade focuses a lot on the applied learning. Assignments and exercises on the Jupyter notebook workbooks is a huge plus point of this course. Every section has a custom exercise meant to help the student internalise the concepts taught in the section, which is followed by a full solution walkthrough of the practise questions. There is also a Capstone Project and over a dozen fully implemented Machine Learning portfolio projects. Real world information set is provided to the students for the different machine learning algorithms. Also the students are provided with ways to get more data sets to acuminate their skills via resources like Kaggle.
This form is targeted at beginner and intermediate Data Scientists and touches just about everything to some degree, from Python basics to NLP to deep learning. The participants are expected to have some programming experience, preferably in Python.
Key Highlights
- Learn to use Python for Data Science and Machine Learning
- Larn to use Spark for Big Data Assay
- Larn to implement Machine Learning Algorithms
- Learn to apply Pandas for data analysis, NumPy for numerical data, Seaborn for statistical plots, Matplotlib for python plotting, Plotly for interactive dynamic visualizations and SciKit-Learn for motorcar learning
- Explore Natural Language Processing and Spam Filters
- Admission to online community Q&A forums with thousands of information science students
- Over 150 HD video lectures and fully written out code and notebooks for reference
Duration : 25 hours on-demand video
Rating : four.6
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13. Data Science A-Z™: Existent-Life Data Scientific discipline (Udemy)
This Data Scientific discipline Course is very comprehensive and teaches data science step-past-step through real earth analytics. A very good residuum of theory, practice, real world business concern bug, accept abroad templates and home work exercises go far one of the best courses on data science available online. Regardless of your prior experience with data science, it will help y'all realize your potential to get a data scientist. This course is taught past Kirill Eremenko who has created 63 courses on Udemy and has taught over 900,000 students and is certainly one of the best tutors in the business.
This course includes over 21 hours of on-demand video and is split into 4 main parts (with several lectures in each part) representing steps in information science journey:
- Start part covers visualization and in particular how to conduct data mining in Tableau.
- 2nd part teaches theory of modelling from footing up. You receive footstep-by-step blueprint to creating a data model and learn to implement those steps to build a robust customer segmentation model. You lot also learn to admission your models and will be provided with associated templates.
- 3rd role focuses on data training. With realistic practise it prepares you for challenges of the real world. You acquire to clean data sets and load them up in databases. You besides acquire foundations of SQL and how to leverage information technology for data science projects.
- Last function focuses on importance of advice in data science presentations including tips and tricks to effectively present your findings.
Key Highlights
- Develop proficient understanding of data science tools – SQL, SSIS, Tableau and Gretl
- Create Simple Linear Regression, Multiple Linear Regression, Logistic Regression
- Use Astern Emptying, Forrad Pick, and Bidirectional Elimination methods to create statistical models
- Operate with Imitation Positives and False Negatives and know the difference
- Empathise multicollinearity
- Build the CAP curve in Excel and derive insights
- Apply three levels of model maintenance to prevent model deterioration
- Clean data and look for anomalies
Duration: 21 hours on-need video, iv articles
Rating: 4.5
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14. Google Information Analytics Professional Certificate (Coursera)
This Data Analytics certificate programme past Google on Coursera provides learners all the skills they demand to notice an entry-level job in the field of data analytics. They larn how to collect, transform, and organize data in society to help draw new insights, make predictions and bulldoze informed business organisation decisions. The program likewise covers the platforms and mean solar day-to-day tools used by a data analyst such as, Spreadsheets like Excel or Google Sheets, SQL for information extraction, Tableau for data visualization, R programming, RStudio, and R packages including the Tidyverse package.
The curriculum of this data analytics certification program has been adult by field of study-matter experts and senior practitioners at Google, forth with input from height employers and industry leaders, like Tableau, Accenture, and Deloitte. It is a very practical program where learners are introduced to the world of data analytics through a serial of 7 courses and an optional Capstone project. Following topics are covered in the courses:
- Overview of information analysis procedure
- Information types, formats and structures
- Using data to solve problems
- How to collect data for analysis
- how to access databases and extract, filter, and sort the data they contain
- Cleaning and transforming data
- How to analyze information
- Information storytelling with visualizations
- Using R programming to supercharge your analysis
Apart from video lessons, the program includes a plethora of easily-on activities, assessments, quizzes and assignments. Capstone project provides opportunity to complete a instance study that you can share with potential employers to showcase your new skill prepare. Those who complete the certificate plan will have access to career resources and be continued direct with Google and over 130 partner employers hiring for open entry-level roles in data analytics.
Cardinal Highlights
- Learn the basics of beingness a data annotator, including the tools needed to master the day-to-day of an analyst
- Learn the best practices for organizing data and keeping it secure
- Explore the central concepts associated with programming in R
- Exercise-based assessments that simulate real-world data analytics scenarios
- Improve your interview technique and resume with access to Google career resource
- Learn at a stride and schedule right for you
Duration : six months, ten hours per week
Rating : 4.7
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15. Data Science: Foundations using R Specialization by Johns Hopkins (Coursera)
This foundational Data Scientific discipline program is offered by Johns Hopkins University and is taught by iii eminent professors Jeff Leek, Roger D Peng and Brian Caffo of the Johns Hopkins Bloomberg Schoolhouse of Public Wellness. It covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research.
The specialization has 5 courses that clock in around lxx hours of video content. These v courses are the same courses that make upwardly the first half of the Johns Hopkins' Data Scientific discipline Specialization. Information technology is intended for students who don't have much prior feel simply are looking to get started in Data Science and want to complete the foundational part of the field of study start earlier progressing to the more than advanced topics.
Following are the five courses that contain this Data Science specialization:
- The Data Scientist's Toolbox – Provides an overview of the data, questions, and tools that information analysts and information scientists work with like version command, markdown, git, GitHub, R, and RStudio
- R Programming – Discusses how to program in R and how to use R for effective data analysis
- Getting and Cleaning Information – Covers the basics needed for collecting, cleaning, and sharing data
- Exploratory Data Assay – Covers the essential exploratory techniques for summarizing data
- Reproducible Research – Covers the concepts and tools backside reporting modern data analyses in a reproducible manner
Each form contains several working examples and culminates in a projection that involves implementing the concepts and skills covered in the course like installing tools, programming in R, cleaning information, performing analyses, as well as peer review assignments.
Primal Highlights
- Proceeds foundational knowledge and prepare to written report advanced topics of Data Scientific discipline and Machine Learning
- All-time fit for students or professionals with minimal feel looking to enter the field of Data Science
- Learn how to read data into R, access R packages, write R functions, debug, profile R lawmaking, and organize R code
- Explore the plotting systems in R as well equally some of the basic principles of constructing data graphics
- Acquire common multivariate statistical techniques used to visualize high-dimensional data
- Acquire about the core tools for developing reproducible documents
Duration : 5 months, eight hours per week
Rating : 4.6
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xvi. Introduction to Information Science Specialization by IBM (Coursera)
This is a very well-rounded foundational course in Data Scientific discipline designed by IBM and offered on Coursera platform. It is intended for learners with lilliputian or no prior experience who wish to make a career in data scientific discipline and prepare them for further advanced learning in this field.
This introductory Data Scientific discipline program consists of four courses that build foundational information science skills. Information technology starts with an agreement of what Data Scientific discipline is and the various kinds of activities that a Data Scientist performs. Then information technology moves to some of the most popular data science tools, their features, and how to use them (similar Jupyter Notebooks, RStudio IDE, Apache Zeppelin and Data Science Feel). After that it teaches students about methodology involved in tackling data scientific discipline issues. The specialization also introduces students to relational database concepts and the use of SQL to query databases.
Several projects and easily-on labs are included to allow students to practice and test the concepts taught in the courses. They are provided existent-earth data sets and several exercises that require querying these information sets using SQL from Jupyter notebooks.
After completing all the 4 courses and projects in the specialization, learners receive an IBM Badge as a Specialist in Data Science Foundations forth with a certificate of completion.
Key Highlights
- Best fit for learners wanting to build foundational skills in Data Science
- Explore various open up source tools used by Data Scientists, similar Jupyter notebooks, Zeppelin, R Studio and Watson Studio
- Create and admission a database case on cloud
- Learn advanced SQL concepts like filter, sort, group results, use built-in functions, admission multiple tables
- Work with real databases, existent information science tools and real-world datasets
- Learn to admission databases from Jupyter using Python
Duration : 3-4 months, iii-four hours per week
Rating : 4.6
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17. Machine Learning Specialization by Academy of Washington (Coursera)
This Specialization in Machine Learning has been created by the leading researchers at the University of Washington for scientists and software developers who desire to expand their skills into data scientific discipline and car learning. There are 4 courses in this plan that delve into major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. Students larn to clarify large and circuitous datasets, create systems that suit and improve over time, and build intelligent applications that tin make predictions from data.
The instructors Emily Fox and Carlos Guestrin are both Amazon Professors of Auto Learning at University of Washington. They explain the concepts clearly and follow them with a worked out instance for a better grasp.
This specialization is a practiced blend of theoretical and practical. Every module has conceptual quizzes as well as ane or two Jupyter Notebook assignments. The quizzes and exercises do an excellent job of reinforcing the concepts from the video instruction. The application assignments offer proficient insights into the common data science problems.
The program assumes some noesis of Python and data structures as most assignments use Python. But pretty much anyone with knowledge of basic math and some feel in computer programming can have upwards this specialization to acquire the primal concepts of auto learning and how to derive intelligence from data.
Key Highlights
- Learn to use machine learning techniques to solve circuitous existent-world bug
- Series of applied case studies to gain hands-on experience with motorcar learning
- Learn to build an end-to-end awarding that uses automobile learning at its core
- Learn to employ regression, nomenclature, clustering, retrieval, recommender systems, and deep learning
- Includes lectures defended to working with Graphlab Create library
- Learn to assess and improve an algorithm'south performance
- Existent world datasets are used for auto learning algorithms throughout each course
Elapsing : seven months, iii hours per week
Rating : 4.vii
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xviii. SQL for Data Scientific discipline by UC Davis (Coursera)
Being able to retrieve and piece of work with data is a very important skill for a good Data Scientist. This means 1 has to exist well versed in SQL, which is the standard language for communicating with database systems. This course created by University of California, Davis and hosted on the Coursera platform, aims to give learners a primer in the fundamentals of SQL and working with information so that they tin can begin analyzing it for data scientific discipline purposes.
This course is for beginners and does not assume any prior cognition of SQL. It therefore starts with the nuts and gradually builds on that foundation. In no time students are able to write both simple and circuitous SQL queries to select data from database. The following topics are covered:
- Differences between one-to-ane, one-to-many, and many-to-many relationships within databases
- Unlike types of information like strings and numbers
- Create new tables and move data into them
- Common SQL operators and how to combine the data
- Bones math operators, as well as aggregate functions like Boilerplate, COUNT, MAX, MIN, and others that are used to analyse the data
- Subqueries and Joins in SQL
- Methods to filter and skin down query results
- Instance statements and concepts like data governance and profiling
Apart from pre-recorded video lectures, there are machine-graded and peer-reviewed assignments. Students also go access to community give-and-take forums. The course is self-paced and designed to teach ane SQL skills fast.
Key Highlights
- Learn to interpret the structure, pregnant, and relationships in source data and utilise SQL as a professional to shape the data for targeted analysis purposes
- Learn tips and tricks to apply SQL in a data science context
- Learn to use SQL commands to filter, sort, and summarize information
- Practise using real-globe programming assignments
Duration : Approx. 14 hours
Rating : 4.6
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19. Mathematics for Car Learning Specialization by Royal College London (Coursera)
Mathematics is one of the about important foundational blocks of Machine Learning. A practiced base in mathematics helps learners to understand better the concepts underlying various algorithms and APIs. Most ofttimes than not, working professionals lose touch with basic mathematical concepts and therefore struggle to chronicle to how they're used in Information Scientific discipline. This specialization offered past Majestic College of London aims to bridge that gap and get learners up to speed in the underlying mathematics. It builds an intuitive understanding of mathematical concepts, and how they relate to Machine Learning and Data Science, thus preparing learners for several higher level courses in Machine Learning and Data Science.
There are three courses in this specialization –
- Linear Algebra – It discusses linear algebra and how it relates to vectors and matrices. It also looks at what vectors and matrices are and how to work with them and how to use them to solve problems
- Multivariate Calculus – It is an introduction to the multivariate calculus required to build many common motorcar learning techniques.
- Dimensionality Reduction with Primary Component Analysis – Information technology introduces the mathematical foundations to derive PCA, a fundamental dimensionality reduction technique. This form is of intermediate difficulty and requires some Python and numpy knowledge.
There are exercises and quizzes in each course that give yous more insight in the concepts learnt and help to solidify the learning. Equally part of assignments of this specialisation, students are required to produce mini-projects with Python on interactive notebooks. This helps them to apply the skills learnt to real world problems. Since this plan is aimed at maths underlying data driven applications, one can go a lot of working knowledge out of it.
Key Highlights
- Gain the prerequisite mathematical cognition to have more advanced courses in machine learning
- Implement mathematical concepts using real-globe data
- Understand important mathematical concepts to exist able to implement PCA all by yourself
- Learn how calculus is applied in linear regression models and in the training of neural networks
- Empathize how orthogonal projections work
- Derive PCA from a projection perspective
Duration : 4 months, 4 hours per calendar week
Rating : 4.v
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20. Microsoft Professional person Plan in Information Science (edX)
The Microsoft Professional Program in Data Science has been developed by Microsoft in collaboration with leading universities and employers and is available on online learning platform edX. In this plan you volition larn data scientific discipline fundamentals, fundamental tools and programming languages from industry experts.
This Microsoft data science certification comprises of 3 units and a final capstone projection taught over ten courses. Learners need to complete all ten courses and reach a lxx% pass rate to earn MPP (Microsoft Professional Program) Information Science certificate. Some courses requite learners the option to choose between dissimilar technologies. For example in Unit i (Fundamentals), you could choose between Analyzing and Visualizing Data with Excel or with Power BI. Similarly in Unit of measurement 3 (Practical Data Scientific discipline), i has a choice between learning R or Python for programming form. Though you lot could accept both courses, but 1 must be completed to satisfy the requirements for graduation.
The program courses include Analyzing and Visualizing Information, Querying Data with Transact-SQL, ethics and Constabulary in Data & Analytics, Creating data models using MS Excel or Power BI, Auto Learning with R or Python,Data Science Research Methods, Developing Big Data Solutions with Azure Machine Learning, Implementing Predictive Analytics with Spark in Azure HDInsight, Applying statistical methods to information. In all courses equal emphasis is placed on theoretical and applied lessons.
Key Highlights
- Use Microsoft Excel to explore data
- Use Transact-SQL to query a relational database
- Create data models and visualize data using Excel or Power BI
- Apply statistical methods to data
- Use R or Python to explore and transform data
- Follow a data science methodology
- Create and validate auto learning models with Azure Motorcar Learning
- Create an Azure SQL Server Database
- Write R or Python code to build machine learning models
- Utilise data science techniques to common scenarios
- Implement a machine learning solution for a given data problem
Duration: x courses + Final Capstone Project, sixteen to 32 hours per course
Rating: 4.five
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More than Data Science Courses Online
21. Online Information Science Masters Degrees (Coursera)
Coursera hosts a brilliant choice of Online Master'south Degree in Data Science on its platform. These caste programs are offered by the top global data science schools and are taught by the same professors that teach degree courses on campus. An important upside being that these online degrees cost less than half the cost of their on-campus counterparts.
An online Data Scientific discipline Main'south degree is a especially designed graduate program that combines core concepts from mathematics, computer science, statistics, and informatics to leverage insights and help data scientists improve operational and concern processes. It is a bully fit for professionals who are interested in furthering their data scientific discipline career, or interested in building or expanding skills in machine learning, cluster analysis, databases, data visualization, statistics, data mining and more than.
Some of the caste programs i can opt for are:
- Principal of Applied Information Science
By University of Michigan - Chief of Reckoner Science in Data Science
By Academy of Illinois at Urbana-Champaign - MSc in Machine Learning
By Royal College London - Master of Scientific discipline in Data Science
Past University of Colorado Boulder - Master of Information Scientific discipline
Past National Research University Higher School of Economics
These Data Scientific discipline degrees on Coursera include applied projects that apply same programming environments that data scientists use professionally every twenty-four hour period, thus students are better prepared to take on problems in the real globe. Most of these degree programs tin can be completed in as little as two to three years time and participants tin can continue their job while pursuing them instead of having to take fourth dimension off from their job.
Key Highlights
- Designed for aspiring information scientists to learn and apply skills through hands-on projects
- Content developed past globe-class kinesthesia at top-ranked universities of the world
- Focussed on applied real-world learning
- Programs led by the same top-ranked professors that lecture on campus
- Hands-on learning approach with excellent peer-to-peer support
- Work with real data sets from top companies and build a work portfolio that showcases your skills
- Consummate flexibility to pursue your information scientific discipline pedagogy on your own time
Duration : Cocky-Paced
Rating : 4.6
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22. Data Scientist Career Path for Beginners (Codecademy)
This Data Scientific discipline program from Codecademy helps you main the skills you lot need to become a data scientist. Y'all will learn to analyse data with SQL and Python and build automobile learning algorithms. Y'all volition too acquire NumPy, pandas, matplotlib, scikit-acquire and more. No prior experience in data science is needed to have upwards this course.
Beginners are welcome to enrol in the program every bit everything is taught from scratch. This Codecademy Data Scientist grade is comprised of thirteen lessons that are estimated to take 35 weeks of small office-time effort for a beginner.
Fundamental Highlights
- Learn SQL to talk to databases and manipulate tables
- Learn fundamental statistics and analysis techniques
- Apply Python for statistical analysis and create data visualizations to encounter the large picture
- Observe how to use supervised learning techniques, in which algorithms learn from many examples of past outcomes
- Larn how to perform learning on a dataset when we don't have any of the answers to brainstorm with
- How to create charts and graphs to illustrate your findings
- Acquire Data Visualization on real globe datasets
- Practice Sublime Lime's line graphs
Duration: 35 weeks
Rating: 4.5
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23. Gratuitous Coursera Information Science Courses (Coursera)
Coursera offers data science courses, specializations, professional person certificate programs, and online degrees from elevation universities and data science schools all over the world. It has as well partnered with industry leaders like IBM to offer programs that impart required data science and machine learning skills to set up learners for existent world. Their catalogue includes a wide range of popular online courses in subjects ranging from foundations like Python and R programming to advanced deep learning and bogus intelligence applications.
Some top choices include courses and specializations in Data Scientific discipline from Johns Hopkins University, Introduction to Information Scientific discipline, Applied Information Science and Practical AI by IBM, Machine Learning by Stanford Academy, Deep Learning past Andrew Ng, Practical Information Science with Python and Python Data Structures past University of Michigan, Business organisation Analytics by University of Pennsylvania, Duke Academy's Excel to MySQL: Analytic Techniques for Business and Excel Skills for Business organization past Macquarie University.
Coursera also offers data science degrees from top-ranked colleges like Academy of Illinois, Regal Higher London, University of Michigan, University of Colorado Boulder, and National Research University Higher Schoolhouse of Economics.
Primal Highlights
- Several options to learn important skills in Data Science like Python programming, R programming, Data Visualization, Analytics, Statistics, Large Data etc.
- Opportunities to collaborate with other learners from all around the world
- Courses from the globe's best instructors and universities
- Courses include recorded auto-graded and peer-reviewed assignments, video lectures, and community discussion forums
- Capstone projects to showcase your expertise to potential employers
- Flexible learning at your own stride
- Certificate of Completion upon completing the lectures and hands-on projects
- Audit the courses for free
Elapsing : Self-Paced
Rating : 4.half dozen
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24. Free edX Data Science Courses (edX)
edX offers a wide variety of Data Science programs that can help to advance your learning in this field. Their library includes more than 200 professional certificates, micromasters programs, master'south degree programs and private courses from acme-ranked colleges and universities in the earth. There are courses for all branches of information science like Machine Learning, Python programming, R programming, SQL, Information Analysis, Excel and Business Analytics, Probability and Statistics etc.
At that place are programs for complete beginners as well as those for avant-garde level users. For those who have a groundwork in statistics and estimator science and desire to expand their skills in data science can choose from following notable options:
- MicroMasters in Statistics and Information Scientific discipline by MIT
- Professional Document in Data Science by Harvard
- Professional Document in Data Science past IBM
- Information Scientific discipline Fundamentals by Microsoft
- MicroMasters in Data Scientific discipline by University of California, San Diego
- MicroMasters in Analytics:Essential Tools and Methods past Georgia Tech
At that place are several introductory courses in Python and R for data scientific discipline as well every bit foundational courses in probability and statistics. One can even opt for Master's Degree in Analytics from Georgia Tech.
Fundamental Highlights
- Real college courses from Harvard, MIT, and more of the globe'due south leading schools and universities
- Beginner, intermediate and avant-garde data science programs to match individual current and goal skill levels
- Larn from assignments and hands-on projects created to reflect real globe challenges
- Selection to audit the courses for free and add a verified certificate at a small fee
- Flexible learning at one's own pace and comfort
Duration : Self-Paced
Rating : 4.6
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25. Udemy Data Science Courses (Udemy)
Udemy offers highly-rated information science certification courses created by industry experts and professionals. Whatever be your feel or skill level, you can find a grade that suits your requirements from the vast catalogue of data scientific discipline courses on Udemy. There are courses for every topic and aspect of information scientific discipline from auto learning to data analysis and visualization to python programming, R programming, artificial intelligence and statistics.
Some of the almost popular courses include:
- Car Learning A-Z™: Easily-On Python & R In Information Science
By Kirill Eremenko, Hadelin de Ponteves - Python for Information Science and Machine Learning Bootcamp
Past Jose Portilla - The Information Science Grade 2020: Consummate Data Science Bootcamp
By 365 Careers - R Programming A-Z™: R For Data Science With Real Exercises
By Kirill Eremenko - Machine Learning, Data Science and Deep Learning with Python
Past Frank Kane - Statistics for Data Science and Business Analysis
By 365 Careers - Data Science and Motorcar Learning Bootcamp with R
Past Jose Portilla - SQL & Database Blueprint A-Z™: Acquire MS SQL Server + PostgreSQL
By Kirill Eremenko, Ilya Eremenko - Data Scientific discipline Career Guide – Interview Training
By Jose Portilla
Udemy likewise offers many free data science courses on various topics that are worth because.
Cardinal Highlights
- Learn from summit instructors and experts in the Information Science domain
- Cull from diverseness of courses that comprehend every aspect and branch of Information Science like car learning, information analysis, data mining, deep learning and more
- Become lifetime access to all course materials and any futurity updates
- Courses include video lessons, quizzes, exercises, downloadable resources and supplemental material
- All courses come with a 30 day Udemy money back guarantee
Elapsing : Self-Paced
Rating : 4.5
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Source: https://www.codespaces.com/best-data-science-certifications-courses-tutorials.html
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