Foundations of Data Science
Get certified as a Data Scientist with our Data Science Training Course
By reading this, you've already taken your first steps on the path the becoming a data scientist. Here are a few reasons to stick along!
Data Science is fast becoming one of most sought after professions in India and around the world.
More than 1.5 Lakh job openings for Data Scientists projected in 2020, increasing by 62% from 2019.
Data is everywhere, it is a universal currency. Learning how to gain insights from data is an invaluable skill to have.
If you are familiar with programming (in any language) and comfortable with mathematics at 12th standard (high school) level, then you should be able to follow along with this course. This course is well suited for the following learning objectives:
Understand the value of data science and the process behind using it.
Learn the fundamentals of statistics and probability required for data science.
Use Python to gather, store, clean, analyse, and visualise data-sets.
Apply statistical methods to formulate and test data hypotheses
Apply statistical inference to uncover relationships within data-sets
Understand the role of ML and DL in the data science pipeline
Understand real-world challenges with several case studies
Mitesh Khapra and Pratyush Kumar
Hello!
Course Discussion Forum
Information Questionnaire
Introduction
What is Data Science?
Collecting Data
Storing Data
Processing Data
Describing Data
Statistical Modelling
Algorithmic Modelling
Why is Data Science so popular today
Are AI and Data Science related?
Problem Solving
Knowledge Representation & Reasoning
Decision Making
Communication, Perception & Actuation
The Myths of Data Science
The Path to Data Science
Feedback: Introduction
Week-1 Quiz Test (Graded & compulsory)
Week-1 Quiz Explained (optional)
Engineering Aspects of Data Science
System Perspective of Data Science
CRISP - DM_Business Understanding
CRISP - DM_Data Understanding, Preparation & Modelling
CRISP - DM_Evaluation & Deployment
Programming Tools
Why Python?
Python - Libraries
Summary
Feedback: Engineering Data Science Systems
Week 2 Part-1 Quiz Test ( Graded & Compulsory)
Week 2 Part-1 Quiz Explained (Optional)
Introduction to Statistics
What is Statistics
How to select a Sample
How to Design an Experiment
How to Describe & Summarise Data
Why do we need Probability Theory?
How do we give guarantees for estimates made from sample
What is a hypothesis & How do we test it?
How to model relationship between variables?
How well does the model fit the data?
Summary
Feedback: What is Statistics
Week-2 Part-2 Quiz Test (Graded & compulsory)
Week-2 Part-2 Quiz Explained (Optional)
Join the course forum and discuss
Getting started with Python
Google Colab
Printing & Basic Data Types
Variables
Integers, Floating Points, Boolean types & Input
Processing Strings, Integers & Floating Points
If, For, While Blocks
Functions
Download: Week 3 Course NoteBook
Assignment Problems
Week 3 Assignment 1 Questions
Week 3 Quiz Test (Graded & Compulsory)
Week 3 Quiz Explained (Optional)
Solution to Assignment Problem 1 - Part 1
Solution to Assignment Problem 1 - Part 2
Solution to Assignment Problem 2 - Part 1
Solution to Assignment Problem 2 - Part 2
Download: Week 3 Assignment Solutions
Feedback: Getting started with Python
Bog Contest 1 - Winners
Introduction to Descriptive Statistics
Different types of Data
How to describe Qualitative Data?
Course Insights
How to describe Quantative Data? Histograms
Histograms Continued...
Typical Trends in Histograms
Uses of Histograms in ML
Stem and Leaf Plots
How to describe relationship between variables? Scatter Plots
Uses of Scatter Plots in ML
Summary
Feedback: Descriptive Statistics Part 1
Week 4 Quiz test (Graded & Compulsory)
Week 4 Quiz Explained (optional)
Commenting and Error Handling
Lists
Lists - Continued
Solution - Exercise problem on Lists
Tuples & Sets
Dictionaries
(Solution - Exercise problem on Dictionaries ) & (Exercise problem in Design Thinking)
Solution - Exercise problem on Design Thinking
File Handling - Read
File Handling - Write
Solution Parts 1, 2 (Exercise on most common words)
Solution Part 3 (Exercise on most common 2-grams)
Feedback: Python (contd)
Week 5 Quiz test (Graded & Compulsory)
Week 5 Quiz Explained (Optional)
Download: Week 5 course notebook, sample text file
Week 5 Assignment - Download (Compulsory)
Week 5 Assignment Solutions - Download (Optional)
Python Data Objects Reference NoteBook
Introduction - Measures of Centrality and Spread
Different measures of Centrality
Characteristics of Measures of Centrality
Sensitivity of the Measures of Centrality to Outliers
What do the measures of Centrality look like for different types of distributions?
Compute median from a Histogram
Compute mean from a Histogram
Compute Mode from a Histograms
Effect of Transformations on the measures of centrality
Summary
Feedback: Descriptive Statistics Part 2
Week 6 Quiz Test (Graded & Compulsory)
Week 6 Quiz Explained (optioinal)
Introduction to Measures of Spread - Percentiles
Procedure for Computing Percentile
Alternative methods for Computing Percentile - Part - 1
Alternative methods for Computing Percentile - Part - 2
Frequently used Percentile
Compute the Percentile rank of a value in the data
Effect of Transformation on Percentiles
Summary Percentiles
Measures of Spread
Measures of Spread (Variance)
Why we square the Deviations ?
What does the variance tell us about the data ?
Effect of Transformations on Measures of spread
How do you use mean & Variance to Standardise data ?
Summary Measures of Spread
What are Box Plots ?
Feedback: Descriptive Statistics Part 3
Week 7 Quiz Test (Graded & Compulsory)
Week 7 Quiz Explained (Optional)
Python Data Containers - Reference
W8 Data Files - Download
NumPy
High Dimensional Array & Creating NumPy Array
Indexing
Numpy Operations
Problem Solution
Broadcasting
File handling
Stats with Numpy
Rules of Statistics
Case Study & Problems
Problem Solution Part 1
Problem Solution Part 2
Problem Solution Part 3
Lecture Notebooks - Download
Feedback: Numpy
Numpy - Additional Exercises
Week-8 Quiz test (Graded & Compulsory)
Week-8 Quiz Explained (Optional)
Instructions
Form for accessing the Deep Learning Course (2 month validity)
Bernoulli, Binomial, and Poisson distributions
Continuous random variable
Uniform and normal distributions
Data visualisation with Seaborn
Simulating probabilistic events
Break
Sampling strategies
Distribution of sampling statistics (mean, variance, proportion)
Central Limit Theorem
Sampling strategies with Python
Demonstration of central limit theorem
Practice case study
Interval estimation for mean (variance known)
Interval estimation for mean (variance unknown)
Demonstration in Python
Anatomy of Hypothesis Testing
Type I and Type II Errors
Single sample mean with known variance
Single sample mean with unknown variance
Demonstration in Python
Single sample variance
Single sample proportion
Demonstration in Python
Two population mean known variance
Two population mean, known variance, small sample
Two population mean, known variance, large sample
Paired t-test
Two population, proportion
Demonstration in Python
One factor analysis
Two factor analysis
Demonstration in Python
Model
Estimating parameters
Measuring goodness of fit
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Student Testimonials from our previous offering: PadhAI Deep Learning
Each week, we will release 2 to 3 hours of video content. We recommend 2 to 3 hours of self-learning and practice. Thus, a weekly commitment of 4 to 6 hours is required. The duration of the course is for 18 to 20 weeks.
However, in case you are unable to find this time due to other commitments, you can do the course at your own pace and complete it within any time within one year.
Yes, if you complete the entire course and finish the assignments, you will receive a certificate from One Fourth Labs. This is digitally signed and can be shared on LinkedIn and other websites.
Each course in the PadhAI One Data Science series will have a separate certificate.
You will have access to the course content (videos, assignments, community) for 1 year from the start of the course.
The Foundations in Data Science course focuses on the basics of statistics and Python programming for data science. These fundamentals are required for many job roles.
Also, in the machine learning course, we will assume a background in these areas. If you are confident about the topics enlisted in the syllabus, then you can directly join the Machine Learning course that begins later this year.
No, we do not provide any computational resources. The course platform only hosts the video lectures and assignments. All programming assignments and projects will be done on Google Colaboratory, which is a freely available resource. In the course, we provide a tutorial on how to use Google Colaboratory. It is therefore sufficient to have a standard computer and a good internet connection.
You will have access to the PadhAI course community where you can post your queries. Dedicated TAs will answer them. You are also encouraged to interact with your peers and learn together.
While data science is a highly sought after job role, we do not provide any placement guarantee or support.