In this course you will learn basics of the python programming

## Outline

• Introduction
• Conditional Statement
• Lopping
• Control Statement
• String Manipulation
• List
• Tuple
• Dictionaries
• Functions
• Modules
• Input/Output
• Exception Handling

Content of Data Science
• Introduction
• Numphy
• Panda
• Mathplotlib
• Random Numbers
• Normal distribution
• Binomialrandom Numbers

Descriptive Statistics:
• Mean, Mode, Median
• Variance, Range

Pandas Library and Operations:
• Introduction to Pandas.
• Series

Group by
• Data Frames
• Merging, Joining, and
• Concatenating
• Missing Data
• Operations
• Pandas Exercises

Potting Using Pandas and Interpretation
• plot.barh
• pot.area
• plot.density
• plot.hist
• plot.line
• plot.scatter
• plot.bar
• plot.box

Descriptive Statistics (Theory, Examples and Code)
• Statistics Concepts
• Random variable
• Mean, Mode, Median,
• Quartiles, Percentile

Probability Distribution (Theory, Examples)
• Binomial distribution
• Poisson distribution
• Normal distribution

Correlation: (Theory, Examples and Code)
• Positive correlation
• Negative correlation
• Perfect and No correlation

Theory (Theory ,Example, Code)
• Intersection
• Union
• Difference
• Disjoint

## Who can Join

• This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

## Course Outcome

By the end of this course, students will be able to

• Take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
• Understand techniques such as lambdas and manipulating csv files
• Describe common Python functionality and features used for data science
• Query DataFrame structures for cleaning and processing
• Explain distributions, sampling, and t-tests