About Introduction to Data Science Course
Why pursue Data Science:
 Data Science is the hottest field in the industry right now
 Data Scientists are one of the most demanded professionals
 There are so many data science algorithms to build predictive models, such as linear regression, logistic regression, decision trees and random forests. Keep learning, keep growing!
 The potential of data science is limitless  spanning across industries, roles and functions
What will you learn in the ‘Introduction to Data Science’ course?
 Understand what data science is
 The various and diverse applications of data science
 Common data science terminologies
 Python for data science – the most popular data science programming language
 Python from scratch – no previous programming experience required!
 Understand how to use Jupyter Notebooks for data science
 Get familiar with popular Python libraries for data science like Pandas and NumPy
 Core Statistics for data science  Descriptive Statistics and Inferential Statistics
 Measures of central tendency  mean, median, mode
 Standard deviation
 Outlier detection
 Correlation
 Hypothesis Testing
 Confidence Intervals & margin of error
 Chisquared test
 Probability concepts for data science
 Introduction to Probability
 Central Limit Theorem
 Bernoulli Trials
 Introduction to core machine learning algorithms for data science
 Types of predictive models in data science
 Overview of endtoend data science process
 Data extraction and data cleaning
 Basics of machine learning model building
 Linear regression
 Logistic regression
 Decision trees
 Random forests
 And much, much more!
 Plenty of handson examples and multiple realworld industryrelevant data science projects
Why take Introduction to Data Science course?
Ideal way to start your Data Science Journey
Introduction to Data Science course has been created keeping a data science beginner in mind. This course provides everything you need to start your journey in data science.
Easy to understand content
The biggest challenge beginners face is that most of the courses explain data science as a difficult mathematical subject. Not us! We simplify data science and machine learning with easy to understand videos and help you build intuition on data science concepts.
Experienced Instructors
All the material in this course was created by Analytics Vidhya instructors who bring in immense experience of data science with them. All our instructors have years of experience in data science and analytics.
Industry collaboration
Entire course has been vetted and created along with experts from data science industry. This ensures relevance in industry and enabling you with the content which matters the most.
Real life projects and problems
All projects in the course are based on real life data science problems. No academic datasets are bring used to ensure that you are ready for real life problems.
Prerequisites
This course requires no past knowledge about Data Science or any tool.
Introduction to Data Science Course Curriculum

1
Welcome to the Course
 DataHack Summit 2019  India’s largest Applied Artificial Intelligence and Machine Learning Conference

2
Course Handouts
 Course Handouts

3
Introduction to Data Science
 Getting Started
 Knowing Each Other
 Data Science Overview FREE PREVIEW
 Exercise1
 Terminologies in Data Science FREE PREVIEW
 Exercise2
 Applications of Data Science
 Exercise3
 Instructor's Introduction

4
Basic Python for Data Science
 Brief Introduction to Python
 Introduction to Python test
 Installation steps for Mac
 Installation steps for Linux
 Installation steps for Windows
 Theory of Operators
 Exercise
 Understanding Operators in Python
 Operators test
 Understanding variables and data types
 Variables test
 Variables and Data Types in Python
 Exercise
 Understanding Conditional Statements
 Exercise
 Implementing Conditional Statements in Python
 Conditional Statements test
 Understanding Looping Constructs FREE PREVIEW
 Exercise
 Implementing Looping Constructs in Python
 Looping Constructs test
 Understanding Functions
 Exercise
 Implementing Functions in Python
 Functions test
 A brief introduction to data structure
 Data Structure test
 Understanding the concept of Lists
 Understanding the concept of ListsReference
 Lists test
 Implementing Lists in Python
 Exercise
 Understanding the concept of Dictionaries
 Exercise
 Implementing Dictionaries in Python
 Dictionaries test
 Understanding the concept of Standard Libraries
 Libraries test
 Reading a CSV File in Python  Introduction to Pandas
 Reading a CSV file in Python  Implementation
 Reading a csv file in Python test
 Understanding dataframes and basic operations
 DataFrames and basic operations test
 Reading dataframes and conduct basic operations in Python
 Reading dataframes and conduct basic operations in Python Test
 Indexing a Dataframe
 Indexing DataFrames test
 Exercise
 Understanding Regular Expressions
 Quiz: Regular Expressions
 Regular Expressions in Python
 Quiz: Regular Expressions in Python
 Instructions
 Quiz
 Python Coding Challenge

5
Understanding Statistics for Data Science
 Introduction to statistics
 Mode of the data
 Mode test
 Understanding the various variable types
 Understanding Variable Types test
 Mean of the data
 Mean test
 Outliers in the datasets
 Outlier test
 Median of the dataset
 Median test
 Spread of the data
 Spread test
 Variance of the data
 Variance test
 Standard Deviation of the data
 Standard Deviation test
 Frequency Tables
 Frequency Tables test
 Histograms
 Histograms test
 Introduction to Probability
 Introduction to probability test
 Calculating Probabilities of events
 Calculating Probabilities test
 Bernoulli Trials and Probability Mass Function
 Bernoulli Trials and PMF test
 Probabilities for Continuous Random Variables
 Probabilities for continuous random variable test
 The Central Limit Theorem
 Central Limit Theorem test
 Properties of the Normal Distribution
 Properties of Normal distribution test
 Using the Normal Curve for Calculations
 Normal Curve for calculations test
 Z score Part 1
 Understanding the Z tables
 Z scores test
 Z score part 2
 Introduction to Inferential Statistics
 Introduction to Inferential Statistics test
 Short Review
 Review test
 Mean Estimation
 Confidence Interval and Margin of Error
 CI and Margin of error test
 Introduction to Hypothesis Testing
 Hypothesis testing test
 Steps to perform hypothesis testing
 Directional Non Directional hypothesis
 Directional and Non Directional hypothesis test
 Understanding Errors while Hypothesis Testing
 Errors while Hypothesis testing test
 Understanding T tests
 Understanding T tests  test
 Degree of Freedom
 TCritical Value
 TCritical Value Test
 Steps to perform TTest
 Steps to perform TTest test
 Conducting One sample T test
 One sample T tests  test
 Paired T tests
 Paired T tests  test
 2 Sample T tests
 2 sample T tests  test
 Chi Squared Tests
 Chi squared tests  test
 Correlation
 Correlation test
 Conclusion
 Module Test
 Instructions
 Quiz
 Statistics Coding Challenge
 Assignment: Share your learning and build your profile

6
Data Manipulation and Visualization
 Sorting Dataframes
 Merging Dataframes
 Quiz: Sorting and Merging dataframes
 Apply function
 Aggregating data
 Quiz: Apply function and Aggregating data
 Basics of Matplotlib
 Data Visualization using Matplotlib
 Quiz: Matplotlib
 Basics of Seaborn
 Data Visualization using Seaborn
 Quiz: Seaborn

7
Predictive Modeling and the basics of Machine Learning
 Introduction to Predictive Modeling
 Predictive Modeling Introduction test
 Types of Predictive Models
 Types of Prediction Models test
 Stages of Predictive Modeling FREE PREVIEW
 Stages of Predictive Modeling test
 Understanding Hypothesis Generation
 Hypothesis Generation test
 Data Extraction
 Data Extraction Test
 Understanding Data Exploration
 Data Exploration Test
 Reading the data into Python
 Reading Data into Python test
 Reading the data into Python : Implementation
 Reading the data into Python : Implementation Test
 Variable Identification
 Variable Identification test
 Variable Identification : Implementation
 Variable Identification : Implementation Test
 Univariate analysis for Continuous Variables
 Univariate analysis for Continuous variables test
 Univariate Analysis for Continuous Variables : Implementation
 Univariate Analysis for Continuous Variables : Implementation Test
 Understanding Univariate Analysis for categorical variables
 Univariate Analysis for categorical test
 Univariate analysis for Categorical Variables : Implementation
 Univariate analysis for Categorical : Implementation Test
 Understanding Bivariate Analysis
 Bivariate Analysis Test
 Bivariate Analysis : Implementation
 Bivariate Analysis : Implementation Test
 Understanding and treating missing values
 Treating missing values test
 Treating missing values : Implementation
 Treating missing values : Implementation Test
 Understanding Outlier Treatment
 Outlier treatment test
 Outlier Treatment in Python
 Outlier Treatment in Python Test
 Understanding Variable Transformation
 Transforming variables test
 Variable Transformation in Python
 Variable Transformation in Python Test
 Basics of Model Building
 Basics of Model Building test
 Understanding Linear Regression
 Linear Regression test
 Implementing Linear Regression in Python
 Linear Regression Implementation Test
 Understanding Logistic Regression
 Logistic Regression test
 Implementation of logistic Regression
 Logistic Regression Implementation Test
 Understanding Decision Trees
 Understanding Decision Tree Test
 Decision tree  Splitting
 Decision tree splitting criteria
 Decision Tree splitting Test
 Implementation of Decision Tree
 Decision Tree Implementation test
 Introduction to Evaluation Metrics
 Evaluation Metrics Test
 Understanding Confusion Matrix
 Confusion matrix Test
 Accuracy
 Accuracy test
 Alternatives of Accuracy
 Alternatives of Accuracy Test
 Precision Recall
 Precision & Recall Test
 Thresholding
 Thresholding Test
 AUC ROC
 AUC ROC Test
 Log Loss
 Log Loss Test
 Evaluation Metrics for Regression
 Evaluation metrics for regression Test
 Adjusted Rsquared
 Adjusted Rsquared Test
 Introduction to Random Forest
 Building a Random Forest
 Hyperparameters of Random Forest
 Implementation of random forest
 Understanding Kmeans
 KMeans Test
 Implementation of KMeans
 KMeans Implementation Test
 Module Test
 Instructions
 Quiz
 Modeling Coding Challenge
 Assignment: Share your learning and build your profile

8
Final Project
 Project 1  Classification
 Project 2  Regression
Project  Identify best Insurance agents
Predict performance of Insurance agents using past data
Project  Sales Prediction for a large Supermarket
Use Data Science to predict sales of products across Supermarkets
Project  Predict survivors from Titanic tragedy (Inclass)
Use data science to identify survivors
Instructor(s)

Founder & CEO
Kunal Jain
Kunal is the Founder of Analytics Vidhya. Analytics Vidhya is one of largest Data Science community across the globe. Kunal is a data science evangelist and has a passion for teaching practical machine learning and data science. Before starting Analytics Vidhya, Kunal had worked in Analytics and Data Science for more than 12 years across various geographies and companies like Capital One and Aviva Life Insurance. He has worked with several clients and helped them build their data science capabilities from scratch. 
Neeraj Singh Sarwan
Neeraj is working as a data scientist at Analytics Vidhya. He has extensive experience in converting business problems to data problems. He has previously taken several corporate trainings and is also an avid blogger. He's a graduate of IITBHU and will be your instructor for the Python and Modeling modules. 
Pranav Dar
Pranav is the editor for Analytics Vidhya. He has experience in data visualization and has been previously involved in the learning & development departments of MNCs. He has taken various trainings on statistics & presentation skills and loves to read, write about anything from data science to machine learning. He will be your instructor for the statistics module.
Here's what our students have to say about our Introduction to Data Science course

Very well organized
Abhilash G
very organized easy to follow course
very organized easy to follow course
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I would definitely recommend this!
Naren Bakshi
The course covers all the 3 aspects of Data science, i.e Programming, Statistics, and the ML part. It also has 2 final projects to let you practice the newly...
Read MoreThe course covers all the 3 aspects of Data science, i.e Programming, Statistics, and the ML part. It also has 2 final projects to let you practice the newly learned skills. It's a 10/10 from me 👍
Read Less 
Just the right course for beginners like me
Umang Verma
I had been trying to get into data science on my own for some time, but this course provided a very good structure and the hands on experience needed to star...
Read MoreI had been trying to get into data science on my own for some time, but this course provided a very good structure and the hands on experience needed to start the journey in a simple manner. The lectures are easy to understand and the course covers basics of Python, Statistics and Predictive Modeling.
Read Less 
Great course for who is just getting start with python an...
Leonardo Silva
Easy going course with handson exercises.
Easy going course with handson exercises.
Read Less 
Good
sandeep1811 sandeep1811
Very nice course. i like it.
Very nice course. i like it.
Read Less
FAQ

Who should take this course?
This course is meant for people looking to learn Data Science. We will start out to understand the prerequisites, and then go on to solve case studies using Data Science concepts.

When will the classes be held in this course?
This is a self paced course, which you can take any time at your convenience over the 6 months after your purchase.

How many hours per week should I dedicate to complete the course?
If you can put between 6 to 8 hours a week, you should be able to finish the course in 4 to 6 weeks.

Do I need to install any software before starting the course ?
You will get information about all installations as part of the course.

What is the refund policy?
The fee for this course is nonrefundable.

Do I need to take the modules in a specific order?
We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.

Do I get a certificate upon completion of the course?
Yes, you will be given a certificate upon satisfactory completion of the course.

What is the fee for this course?
Fee for this course is INR 7,999

How long I can access the course?
You will be able to access the course material for six months since the start of the course.
Money Back Guarantee!
Support for Introduction to Data Science Course
Support for Introduction to Data Science course can be availed through any of the following channels:
 Phone  10 AM  6 PM (IST) on Weekdays Monday  Friday on +918368253068
 Email training_support@analyticsvidhya.com (revert in 1 working day)
 Live interactive chat sessions (https://support.analyticsvidhya.com/ ), Monday to Friday between 7 PM to 8 PM IST.