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11 Free Courses Data Science Learning Path from Newbie to Expert


Data Science is the hottest field of the century. Data science, artificial intelligence (AI) and machine learning are revolutionizing the way people do business and research around the world. Learning Data Science is the best thing you can do for your career and it’s FREE. I have created a learning path for you. Here we list down top free courses available for a Data Scientist to start from basic to advanced, a complete learning path for Data Science. It includes learning about Introduction to Data Science, Data Science Tools and Methodologies, Statistics for Data Science, Predictive Modeling, Python for Data Science, Data Analysis with Python, Data Visualization with Python, Machine Learning with Python, Introduction to Deep Learning and Deep Learning with TensorFlow.
If you are changing your career to Data Science, then you will find following articles very helpful –

1. Introduction to Data Science

ABOUT THIS COURSE

Find out the truth about what Data Science is. Hear from real practitioners telling real stories about what it means to work in data science. This course was formerly named Data Science 101.
TIME TO COMPLETE: 3 Hours

COURSE SYLLABUS

  • Module 1 – Defining Data Science
  • Module 2 – What do data science people do?
  • Module 3 – Data Science in Business
  • Module 4 – Use Cases for Data Science
  • Module 5 -Data Science People

2. Data Science Tools

ABOUT THIS COURSE

Get started with some of the most popular tools for collaborative data science, including RStudio IDE, Jupyter Notebooks, Apache Zeppelin notebooks, and IBM Watson Studio. Use the tools directly on Skills Network Labs, a cloud lab environment that brings powerful open data science tools together so you can analyze, visualize, explore, clean data, run models and create apps.
TIME TO COMPLETE:4 hours

COURSE SYLLABUS

  • Module 1 -Introducing Skills Network Labs
  • Module 2 -Introducing Jupyter Notebooks
  • Module 3 – Introducing Zeppelin Notebooks
  • Module 4 – Introducing RStudio IDE

3. Data Science Methodology

ABOUT THIS COURSE

This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand.
Accordingly, in this course, you will learn:
  • The major steps involved in tackling a data science problem.
  • The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
  • How data scientists think!
TIME TO COMPLETE:5 Hours
AUDIENCE:Data Scientists, Data Engineers, Anyone with interest in Data Science

COURSE SYLLABUS

  • Module 1: From Problem to Approach
  • Module 2: From Requirements to Collection 
  • Module 3: From Understanding to Preparation 
  • Module 4: From Modeling to Evaluation
  • Module 5: From Deployment to Feedback

4. Statistics 101

ABOUT THIS COURSE

Split into five modules, this is a beginner’s course covering the fundamentals of statistics. Start with mean, mode, and median. Then learn about standard deviation using examples from basketball. Learn about probability with dice. Learn what it means to group data by categorical variables, and how you can transform your data into appropriate graphs and charts.
In the final module, using an open dataset, learn whether good looking professors indeed get better teaching evalutions.
This course is taught using SPSS Statistics. No prior experience necesssary.
TIME TO COMPLETE:6 Hours
AUDIENCE:Beginners in statistics

COURSE SYLLABUS

  • Module 1 – Welcome to Statistics!
  • Module 2 – Basic Statistics
  • Module 3 – Summarizing data
  • Module 4- Data Visualization
  • Module 5 – Does Beauty Pay?

5. Predictive Modeling Fundamentals I

ABOUT THIS COURSE

In this course, we will be focusing on predictive modeling fundamentals. These are the mathematical algorithms, which are used to “learn” the patterns hidden in data.
Learn the crucial step in the Big Data Lifecycle: using big data to make decisions!
  • Possess the modeling skills needed by companies all over the world to go beyond storing big data to understanding big data
  • Learn how to use these skills to make decisions such as cancer detection, fraud detection, customer segmentation and predicting machine downtime.
  • Get introduced to the data mining process and modeling techniques using one of the most popular software, IBM’s SPSS Modeler.
  • Learn how to build models on trained data, test the model with historical data, and use qualifying models on live data or other historical untested data.
  • Save or earn companies millions of dollars with your decisions!
TIME TO COMPLETE:5 Hours
AUDIENCE:Business Analysts, Management Consultants, Data Scientists and Tech Professionals

COURSE SYLLABUS

  • Module 1 – Introduction to Data Mining
  • Module 2 – The Data Mining Process 
  • Module 3 – Modeling Techniques
  • Module 4 – Model Evaluation
  • Module 5 – Deployment on IBM Bluemix

6. Python for Data Science

ABOUT THIS PYTHON COURSE

This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours.
Upon its completion, you’ll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this free course is for you.
You can start creating your own data science projects and collaborating with other data scientists using IBM Watson Studio. When you sign up, you get free access to Watson Studio. Start now and take advantage of this platform.
TIME TO COMPLETE:5 hours
AUDIENCE:Anyone interested in learning to program with Python for Data Science

COURSE SYLLABUS

  • Module 1 – Python Basics
  • Module 2 – Python Data Structures
  • Module 3 – Python Programming Fundamentals
  • Module 4 – Working with Data in Python

7. Data Analysis with Python

ABOUT THE COURSE

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!
You will learn how to:
  • Import data sets
  • Clean and prepare data for analysis
  • Manipulate pandas DataFrame
  • Summarize data
  • Build machine learning models using scikit-learn
  • Build data pipelines
TIME TO COMPLETE:8 hours
AUDIENCE:Anyone who wants to use Python to analyze data

COURSE SYLLABUS

  • Module 1 – Importing Datasets
  • Module 2 – Cleaning and Preparing the Data
  • Module 3 – Summarizing the Data Frame
  • Module 4 – Model Development
  • Module 5 – Model Evaluation

8. Data Visualization with Python

ABOUT THIS DATA VISUALIZATION COURSE

“A picture is worth a thousand words”. We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data.
One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions.The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.
TIME TO COMPLETE:10 hours
AUDIENCE:Anyone interested in data science and has completed Python 101 and Data Analysis with Python

COURSE SYLLABUS

  • Module 1 – Introduction to Visualization Tools
  • Module 2 – Basic Visualization Tools
  • Module 3 – Specialized Visualization Tools
  • Module 4 – Advanced Visualization Tools
  • Module 5 – Creating Maps and Visualizing Geospatial Data

9. Machine Learning with Python

ABOUT THIS COURSE

This Machine Learning with Python course dives into the basics of Machine Learning using Python, an approachable and well-known programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
  • Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
  • Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
More important, you will transform your theoretical knowledge in to practical skill using many hands-on labs.
TIME TO COMPLETE:12 Hours
AUDIENCE: Anyone interested in Machine Learning and Python

COURSE SYLLABUS

  • Module 1 – Introduction to Machine Learning
  • Module 2 – Regression
  • Module 3 – Classification
  • Module 4 – Unsupervised Learning
  • Module 5 – Recommender Systems

10. Deep Learning Fundamentals

ABOUT THIS COURSE

Get a crash course on the what there is to learn and how to go about learning more. Deep Learning presents a simplified explanation of some of the hottest topics in data science today:
  • What is Deep Learning?
  • What are are convolutional neural networks?
  • Why is deep learning so powerful and what can it be used for?
  • Be part of a rapidly growing field in data science; there’s no better time than now to get started with neural networks.

COURSE SYLLABUS

  • Module 1 – Introduction to Deep Learning
  • Module 2 – Deep Learning Models
  • Module 3 – Additional Deep Learning Models
  • Module 4 – Deep Learning Platforms and Software Libraries
    1. What is a Deep Learning Platform?
    2. H2O.ai
    3. Dato GraphLab
    4. What is a Deep Learning Library?
    5. Theano
    6. Caffe
    7. TensorFlow

11. Deep Learning with TensorFlow

ABOUT THE COURSE

This Deep Learning with TensorFlow course focuses on TensorFlow. If you are new to the subject of deep learning, consider taking our Deep Learning 101 course first.
TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
TIME TO COMPLETE:10 Hours
AUDIENCE:Anyone interested in Machine Learning, Deep Leaning and TensorFlow

COURSE SYLLABUS

  • Module 1 – Introduction to TensorFlow
  • Module 2 – Convolutional Neural Networks (CNN)
  • Module 3 – Recurrent Neural Networks (RNN)
  • Module 4 – Unsupervised Learning
  • Module 5 – Autoencoders
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