Foundations of Data Science
FIRST COURSE OF PADHAI ONE DATA SCIENCE SERIESp
Mitesh Khapra and Pratyush Kumar
Introduction
FREE PREVIEWWhat is Data Science?
FREE PREVIEWCollecting Data
FREE PREVIEWStoring Data
FREE PREVIEWDescribing Data
Processing 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
Introduction to programming for Data Science
Setting up your system for Data Science
Python basics (Variables, Data types, Functions)
Python basics assignment
Describing datasets (graphs and plots)
Summarising datasets (measures of centrality and dispersion)
Assignment on Statistics
Complex data types (lists, tuples, dictionary, sets)
Iterators and operators on lists
Introduction to NumPy
Measures of spread
Correlation and covariance
Numpy continued (computing measures of centrality and spread)
Introduction to Pandas
Pandas: Indexing and computing statistics
Basics of probability
Counting principles
Random Variables, Expected value, and Variance
Data cleaning, filling missing values, standardisation, normalisation, outlier detection
Data visualisation with Seaborn
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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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.