Topics :

Week 1: Python Basics & Programming Logic 

1.Python setup and IDEs 

• Installing Python and setting up Anaconda 
• Using Jupyter, VS Code, or PyCharm 

2.Basic syntax and indentation 

• Code structure and whitespace importance 

3.Variables and data types 

• Integer, float, string, boolean 
• Type checking with type() 

4.Type casting and conversions 

• Using int(), float(), str() 

5.Basic operators 

• Arithmetic: +, -, *, /, %, //, ** 
• Comparison: ==, !=, >, <, >=, <= 
• Logical: and, or, not 

6.Conditional statements 

• Simple if 
• if-else, elif 
• Nested conditions 

Week 2: Loops & Functions 

1.for and while loops 

• Syntax and flow control 
• Looping over ranges, lists, strings 

2.Loop control 

• break, continue, pass 

3.Function definition and syntax 

• def keyword, indentation, naming 

4.Parameters and return values 

• Positional and keyword arguments 

5.Lambda functions 

• Anonymous functions 
• When and how to use 

6.Recursion 

• Concept and examples 
• Base and recursive cases 

Week 3: Python Data Structures Deep Dive  

1.Lists 

• Creation, indexing, slicing 
• Methods: append, extend, insert, remove 

2.Tuples 

• Immutability and packing/unpacking 

3.Sets 

• Unique values, operations: union, intersection 

4.Dictionaries 

• Key-value access, get(), items(), values() 

5.List comprehensions 

• Syntax and performance benefits 

6.Iteration tools 

• enumerate(), zip(), map(), filter() 

Week 4: File Handling, Modules & Error Handling  

1.File I/O 

• Reading and writing text files 
• Working with CSV using csv module 
• Reading JSON using json module 

2.Working with OS and paths 

• os, pathlib 

3.Importing modules 

• Standard libraries: math, datetime, random 
• Installing third-party packages 

4.Custom modules 

• Creating and importing user-defined modules 

5.Error types 

• Syntax vs. runtime errors 

6.Try-except blocks 

• Handling exceptions gracefully 

7.Finally, raise, and custom exceptions 


 

Week 5: SQL for Data Science – Part 1 

1.Relational database concepts 

• Tables, rows, columns, keys 

2.SELECT queries 

• Syntax, selecting multiple columns 

3.Filtering with WHERE 

• Operators: =, >, <, BETWEEN, IN, LIKE 

4.Sorting with ORDER BY 

• ASC, DESC 

5.Grouping with GROUP BY and HAVING 

• Aggregate functions: COUNT, SUM, AVG, MIN, MAX 

Week 6: SQL: Joins, Subqueries, Window Functions – Part 2 

1.Joins 

• INNER JOIN 
• LEFT, RIGHT, FULL OUTER JOIN 

2.Subqueries 

• Inline subqueries 
• Correlated subqueries 

3.Window functions 

• ROW_NUMBER, RANK, DENSE_RANK 
• LAG, LEAD for time-based analytics 

4.Indexing and optimization 

• Primary vs. secondary indexes 
• Query performance tips 

Week 7: Descriptive Statistics & Probability 

1.Measures of central tendency 

• Mean, median, mode 

2.Measures of dispersion

•  Range, variance, standard deviation, IQR 

3.Probability basics 

• Sample space, events 
• Probability rules 

4.Conditional probability 

• Bayes’ Theorem 

5.Law of Large Numbers 

6.Central Limit Theorem 

 

Week 8: Distributions, Hypothesis Testing & Inference 

1.Probability distributions 

• Normal, Binomial, Poisson 

2.Sampling techniques 

• Random, stratified, systematic 

3.Hypothesis testing 

• Null vs. alternative hypothesis 

4.Statistical tests 

• t-tests, z-tests 
• ANOVA, chi-square 

5.Significance and p-values 

6.Confidence intervals