What is data science, why is it so popular, and why it as the “sexiest job of the 21st century”? In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data. So don’t be put off by the buzzwords. Start learning, gain skills in this hugely in-demand field, and discover why data science is for everyone!
Learn more about the comprehensive education and certification program which develops and validates skills required to advance your career and drive digital transformation. Data science aims to develop strategies and tools for securely and ethically unlocking the value of vast amounts of data. It uses theories and techniques from computer science, statistics, machine learning, and mathematics to comprehend, analyze, and potentially change human, physical, and societal phenomena.
1. Professional Certificate in Data Science (Expertifie)
The Data Science course enables you to understand practical foundations, helping you effectively execute and take up Big Data and other analytics projects. The program covers topics from Big Data to the Data Analytics Life Cycle. Understanding these topics helps in addressing business challenges that leverage Big Data.
Another aspect of this course is that it covers basic as well as advanced analytic methods, and also introduces the participant to Big Data technologies with tools like MapR and Hadoop. Our state-of-the-art-infrastructure allows students to understand the applications of these methods and tools by getting hands-on experience working alongside real-time data scientists. This program has an open approach including a final lab session, which explains various Big Data Analytics challenges by applying the concepts covered during the program with respect to the Data Analytics Life Cycle.
The course is designed for anyone who wishes to understand the concepts of Data Science from a Data Scientist’s perspective. In this course, you’ll learn how to work with large datasets and gain a deep understanding of how to analyse and interpret data. You’ll also learn how to use machine learning algorithms to extract valuable insights and inform future development and strategy.
Key Highlights: –
1. Nurture Your Skillset
With a well-defined advanced certification program, the students will be able to nurture their skillset by making complex things easier to perceive.
2. Live Interaction Sessions
Lively interaction with industry experts, students will develop problem-solving abilities.
3. Get Noticed By Top Hiring Companies
With the advanced certification program, students will get hired by top companies for their skills and analytical approach. Outcome-centric placement options to increase the rate of employability.
4. Certification
Advanced Certification in Applied Data Science after successful completion of the program helps to add value in resume.
5. Real World Case Studies
Learn fundamental data science concepts through motivating real-world case studies
6. 100% Referral Guarantee
100% referral guarantee after clearing internal assessment test
7. Mock Interviews
The course supports upto 6 Mock Interviews.
Duration: 6 Months
Rating: 4.9
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2. IBM Data Science Professional Certificate (Coursera)
IBM Data Science Professional Certificate is a specialization course offered by IBM through the Coursera platform. It is instructed by Rav Ahuja and 12 others IBM professionals. IBM data science professional certificate has a total of 10 courses dealing with data science tools and libraries, python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling along with machine learning algorithms. IBM data science professional certificate can be availed for a monthly subscription of INR 2,906.
Key Highlights
• Learn open source tools used in data science like Jupyter Notebooks, Zepplin, RStudio, and IBM Watson.
• Learn the basics of Python, Pandas, and NumPy
• Build databases, collect and analyze data from them using Python
• Use Python libraries to generate data visualizations
• Well designed content and all the topics are covered elaborately
• Graded Assignments with Peer FeedbackDuration: 11 months (3 hours/week)
Rating: 4.6
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3. The Data Science Course 2020: Complete Data Science Bootcamp (Udemy)
The course provides the entire toolbox you need to become a data scientist. Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow. Impress interviewers by showing an understanding of the data science field. It includes wide variety of animations, quizzes, exercises and bonus materials. One does not need any prior experience to take up this course, everything is taught from the scratch with each topic building on the previous ones so you are prepped to work as a data scientist, handle real-life business cases and can take up more advanced specializations.
Key Highlights
• Understand the mathematics behind Machine Learning
• Perform linear and logistic regressions in Python
• Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
• Learn how to pre-process data
• Carry out cluster and factor analysis
• Unfold the power of deep neural networks
• Apply your skills to real-life business casesDuration: 27 hours on-demand video, 88 articles, 144 downloadable resources
Rating: 4.5
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4. Data Science Nanodegree Courses (Udacity)
With Data Science Nanodegree Course at Udacity, gain real-world data science experience with projects designed by industry experts. Build your portfolio and advance your data science career.
You’ll master the skills necessary to become a successful Data Scientist. You’ll work on projects designed by industry experts, and learn to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the cloud. Learn the data science process, including how to build effective data visualizations, and how to communicate with various stakeholders. Learn to work with data through the entire data science process, from running pipelines, transforming data, building models, and deploying solutions to the cloud.
Key Highlights
• Real-world projects from industry experts
• Technical mentor support
• Career services
• Flexible learning program
• Project Reviews
• Project Feedback from experts
• Content co-created with industryDuration : Self-Paced
Rating : 4.6
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5. Python for Data Science and Machine Learning Bootcamp (Udemy)
This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for very lecture this is one of the most comprehensive course for data science and machine learning on Udemy!
Here a just a few of the topics we will be learning:
- Programming with Python
- NumPy with Python
- Using pandas Data Frames to solve complex tasks
- Use pandas to handle Excel Files
- Web scraping with python
- Connect Python to SQL
- Use matplotlib and seaborn for data visualizations
- Use plotly for interactive visualizations
- Machine Learning with SciKit Learn, including:
- Linear Regression
Key Highlights
• Learn to use Python for Data Science and Machine Learning
• Learn to use Spark for Big Data Analysis
• Learn to implement Machine Learning Algorithms
• Learn to use 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 machine learning
• Explore Natural Language Processing and Spam FiltersDuration : 25 hours on-demand video
Rating : 4.6
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6. Google Data Analytics Professional Certificate (Coursera)
The highly rated course will be your path to a career in data analytics. In this program, you’ll learn in-demand skills that will have you job-ready in less than 6 months. No degree or experience required. Gain an immersive understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job. Learn key analytical skills (data cleaning, analysis, & visualization) and tools (spreadsheets, SQL, R programming, Tableau). Understand how to clean and organize data for analysis, and complete analysis and calculations using spreadsheets, SQL and R programming. You will also learn how to visualize and present data findings in dashboards, presentations and commonly used visualization platforms.
Apart from video lessons, the program includes a plethora of hands-on activities, assessments, quizzes and assignments. Capstone project provides opportunity to complete a case study that you can share with potential employers to showcase your new skill set. Those who complete the certificate program will have access to career resources and be connected directly with Google and over 130 partner employers hiring for open entry-level roles in data analytics.
Key Highlights
• Receive professional-level training from Google
• Demonstrate your proficiency in portfolio-ready projects
• Earn an employer-recognized certificate from Google
• Qualify for in-demand job titles: Data Analyst, Junior Data Analyst, Associate Data AnalystDuration : 6 months, 10 hours per week
Rating : 4.7
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7. Introduction to Data Science Specialization by IBM (Coursera)
Kickstart your career in data science & ML. Build data science skills, learn Python & SQL, analyze & visualize data, build machine learning models. No degree or prior experience required. The course will help you develop hands-on skills using the tools, languages, and libraries used by professional data scientists . You will be able to apply various data science skills, techniques, and tools to complete a project using a real-world data set and publish a report for stakeholders. Import and clean data sets, analyze and visualize data, and build and evaluate machine learning models and pipelines using Python.
Key Highlights
• Best fit for learners wanting to build foundational skills in Data Science
• Explore various open source tools used by Data Scientists, like Jupyter notebooks, Zeppelin, R Studio and Watson Studio
• Create and access a database instance on cloud
• Learn advanced SQL concepts like filter, sort, group results, use built-in functions, access multiple tables
• Work with real databases, real data science tools and real-world datasets
• Learn to access databases from Jupyter using PythonDuration : 3-4 months, 3-4 hours per week
Rating : 4.6
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8. DATA SCIENCE SPECIALIZATION FROM JOHNS HOPKINS UNIVERSITY (Coursera)
This Specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
This Data Science specialization contains 10 courses and the first five courses are also a part of the Data Science: Foundations using R specialization.
• The Data Scientist’s Toolbox
• R Programming
• Getting and Cleaning Data
• Exploratory Data Analysis
• Reproducible Research
• Statistical Inference
• Regression Model
• Practical Machine Learning
• Developing Data Products
• Data Science CapstoneApart from pre-recorded video lectures, there are auto-graded and peer-reviewed assignments. Students also get access to community discussion forums. The course is self-paced and designed to teach one SQL skills fast.
Key Highlights
• Use R to clean, analyze, and visualize data
• Navigate the entire data science pipeline from data acquisition to publication
• Use GitHub to manage data science projects
• Perform regression analysis, least squares and inference using regression models
• Balance both the theory and practice of applied mathematics to analyze and handle large-scale data sets
• Create models using formal techniques and methodologies of abstraction that can be automated to solve real-world problemsDuration : 10 courses, 8 months, 5 hours per week
Rating : 4.6
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9. APPLIED DATA SCIENCE WITH PYTHON SPECIALIZATION BY UNIVERSITY OF MICHIGAN (COURSERA)
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
Syllabus
Course 1: Introduction to Data Science in Python- Offered by University of Michigan. This course will introduce the learner to the basics of the python programming environment, including
Course 2: Applied Plotting, Charting & Data Representation in Python- Offered by University of Michigan. This course will introduce the learner to information visualization basics, with a focus on reporting and …
Course 3: Applied Machine Learning in Python- Offered by University of Michigan. This course will introduce the learner to applied machine learning, focusing more on the techniques and …
Course 4: Applied Text Mining in Python- Offered by University of Michigan. This course will introduce the learner to text mining and text manipulation basics. The course begins …
Course 5: Applied Social Network Analysis in Python- Offered by University of Michigan. This course will introduce the learner to network analysis through tutorials using the NetworkX library.
Key Highlights
• Analyze the connectivity of a social network
• Conduct an inferential statistical analysis
• 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 best practices for creating basic visualizations and charts
• Enhance a data analysis with applied machine learningDuration : 5 courses, 5 months, 7 hours per week
Rating : 4.5
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10. MICROMASTERS PROGRAM IN STATISTICS AND DATA SCIENCE FROM MIT (EDX)
In this MicroMasters program, you will develop a well-rounded understanding of the mathematical and computational tools that form the basis of data science and how to use those tools to make data-driven business recommendations. This MicroMasters program encompasses two sides of data science learning: the mathematical and the applied. Mathematical courses cover probability, statistics, and machine learning. The applied courses cover the use of specific toolkit and languages such as Python, Numpy, Matplotlib, pandas and Scipy, the Jupyter notebook environment and Apache Spark to delve into real world data.
You will learn how to collect, clean and analyse big data using popular open source software will allow you to perform large-scale data analysis and present your findings in a convincing, visual way. When combined with expertise in a particular type of business, it will make you a highly desirable employee.
Key Highlights
• You will learn how to load and clean real-world data
• You will learn how to make reliable statistical inferences from noisy data
• You will learn how to use machine learning to learn models for data
• You will learn how to visualize complex data
• You will learn how to use Apache Spark to analyze data that does not fit within the memory of a single computer
• Learn Data analysis techniques, machine learning algorithms and apply them to real world data sets
• In-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projectsDuration: 5 courses, 2 to 16 weeks per course, 12 to 14 hours per week
Rating: 4.6
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