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Prabhav Jain

Co-founder, Commonlounge. MIT EECS.

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Featured Contributions

2.

course

Data Science Career Path

Overview

This interactive career path teaches you everything you need to become a data science practitioner, with absolutely no background required.

You'll go from knowing no programming to analyzing real-world data problems in Python and delivering valuable insights.

Data Scientists are in demand in virtually every company to drive strategic decisions and power their business. It was ranked the #1 Job by Glassdoor with an average salary of over $120,000.

Trillions of gigabytes of data are being produced yearly, and the number is still growing exponentially. It is estimated that for every person, 1.7 megabytes of data will be produced every second by 2020.

It's not surprise that our society is increasingly becoming data dependent. However, data is only a raw material and extra...

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Syllabus

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Learn Python 3

Category: Data Science and Big Data

3.

course

Foundations of Machine Learning

Overview

We'll start by describing what machine learning is, and introduce a simple learning algorithm: **linear regression + gradient descent**. Using this algorithm, we'll introduce the core concepts in machine learning: *model parameters*, *cost function*, *optimization method*, and *overfitting and regularization*. This section ends with a visual review of these concepts and a tutorial on the different types of machine learning problems.

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What is Machine Learning? Why Machine Learning?

Category: Machine Learning

4.

course

Other topics in Data Science

Overview

This section introduces us to **databases and SQL**, used for storing and managing data used in computer systems. We'll also look at **map reduce**, a programming model that allows us to perform parallel processing on large data sets in a distributed environment. Again, our tutorials will be interleaved with **quizzes and hands-on assignments**.

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Introduction to Databases and SQL with Examples

Category: Data Science and Big Data

5.

course

Data Science End-to-End Workflow

Overview

The first tutorial is a detailed end-to-end example of a **typical data science project and workflow**. The next tutorial contains a list of 10 project ideas (including datasets and suggested algorithms). It is recommended that you do at-least one **end-to-end project** as part of the course.

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End-to-End Example: Using Logistic Regression for predicting Diabetes

Category: Data Science and Big Data

Contributed 51%

6.

tutorial

Correlation Analysis: Two Variables

Correlation analysis can help us understand whether, and how strongly, a pair of variables are related.

In data science and machine learning, this can help us understand relationships between features/predictor variables and outcomes. It can also help us understand dependencies between different feature variables.

For example:

- How strong is the correlation between mental stress and cardiac issues?
- Is there a correlation between literacy rate and frequency of criminal activities?

This tutorial will help you learn the different tech...

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Category: Data Science and Big Data

7.

course

Foundations of Pandas

Overview

The Pandas library introduces a DataFrame, which is basically a table (like a database table or an excel sheet, but in Python). As you will see, this library is extremely useful and versatile.

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Pandas Introduction: DataFrames

Category: Data Science and Big Data

8.

course

An Overview of Data Science

Overview

This course presents a quick, conceptual introduction to **data science** — what it is and examples of data science around us. It introduces **key components** of data science, namely programming, data, statistics, machine learning and big data.

*Note: Unlike our other courses, this one does not have interactive coding blocks since it is more conceptual in nature.*

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Category: Data Science and Big Data

9.

course

Foundations of NumPy

Overview

In this course, you'll learn about NumPy (**Num**erical **Py**thon), which provides vector and matrix primitives in Python.

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Introduction to NumPy

Category: Data Science and Big Data

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