About the Course

In this course you will learn basics of the python programming

Duration: 40 hours

Mode: Classroom or Online Session


  • 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