Contents

1) DATA SCIENCE

• Module 1: PYTHON PROGRAMMING :: > A] Python Introduction & Setting UP the Environment. B] Python Basic Syntax and Data Types. C] Operators in Python. D] Strings in Python. E] Slicing, Indexing & using methods on Lists. F] Working with Tuples. G] Performing Set operation in program. H] Creating Dictionaries & using dictionaries Methods. I} Uses of Python Conditional Statements. J} [ ] Iterating with Loops in Python. K] Getting Started with HackerRank use cases and working on them. L] Using List & Dictionaries comprehension. M] Functions, Anonymous Functions with lambda, filter, map reduce in python. N] Creating & Using Generators. O] Modules, Exceptions and error handling. P] Class and Objects( OOPS). Q] Date and Time, Regex. R] Reading , Writing , Appending, Opening and closing Files. S] APIs the Unsung Hero of the Connected World. T] Python for WEB DEVELOPMENT- Flask, Hands-on Projects.
• MODULE 2- Data Analysis ::- A] Packages- Creating and Importing. B] Web Scraping - Working on Scraping Data from Static Dynamic Websites. C] Exploratory data analysis(EDA) using Pandas and NumPy. D] Data Visualization using Matplotlib, Seaborn, & Plothy, Instagram Reach Analysis. E] Database Access :- Working on SQL Queries. F] MS Excel :- Worksheet, Calculation, Fill Handle, Formula, Quick Functions, Charts & Visualizations, Advance Excel, Tableau and Power BI- Instagram Reach Analysis.
• MODULE 3:: - STATISTICS::- 1] Descriptive Statistics:: >- (Data- Types of data, A Measure of Central tendency -Mean- Median- Mode, A Measure of shape- Variance - standard deviation, Range, IQR, The measure of shape - Skewness, & kurtosis, Probability Distribution- Discrete and Continuous. Uniform Distribution. Expected values, Variance & Means. Gaussian/ Normal Distribution. Properties, mean, variance, empirical rule of normal distribution. Standard normal distribution and Z-Score). 2> Inferential Statistics : (Central Limit Theorem. Hypothesis testing - Null & Alternate hypothesis. Type -I & Type-II error. Critical Value, Significance level, p-value. One - tailed & two-tailed test. T- test- one sample, two-sample, and paired t-test, f-test. One way and two way ANOVA, Chi-Square test.