Python For Data Analytics (2 Day Course)

2 Day hours

Target Audience & Purpose

Python is the most popular and demanding language currently used for Data Analytics, Data science and Machine learning. This course is for people who are looking to perform data analysis/visualization on data and familiar with the basics of python


  • Participants should have knowledge on the basics of Python


  • Understand common data analytics and machine learning Python packages (Numpy, Pandas, Matplotlib and Seaborn)
  • Consume and analyze data from different external sources (RDBMS, Web API’s)
  • Use Python packages to read/manipulate and write data into files(Excel, csv files)
  • Create and handle large data in arrays for faster computation
  • Analyse large datasets to extract Business Insights
  • Handle missing values in files and tables(find and replace values)
  • Analyse any database using SQL querying directly using python
  • Perform all SQL operations using pandas library
  • Apply different visualization graphs using python
  • Establish Database connectivity with RDBMS to perform sql operations

Course Structure

Python For Intermediate – Advanced (2 Day course)


  • What is Data Analytics ?
  • Difference between Data Analytics Vs Data Science
  • Different types of Analytics
  • Installing Anaconda (Advanced package manager for Analytics)
  • Working with python libraries for Data Analytics

Basic Statistics

  • What is Statistics ?
  • Measure of Central Tendency (Mean, Mode and Median)
  • Measure of Spread (IQR, Variance and Standard Deviation)
  • Covariance and Correlation
  • Kurtosis, Skewness
  • Data spread -Empirical Rule

Numpy (Machine learning library)

  • What is Numpy ?
  • Computation power of numpy
  • Intro to Numpy Arrays
  • Creating and converting data structures into numpy arrays
  • Indexing, Data Processing using Arrays
  • Mathematical computing basics

Pandas(Machine learning library)

  • Getting Started with Pandas
  • What is a Pandas Dataframe (DF)?
  • How to load source files(excel,csv, any format ..) into Pandas dataframe
  • How to perform data manipulation and analysis(analytical operations )
  • Getting Descriptive statistics from a dataset
  • How to find and replace missing values
  • Finding and removing duplicates and dropping Null records
  • Combining and Merging Data Frames
  • How to perform all SQL operations using pandas library
  • Read and Write data into the source excel,csv files

External Source Data

  • Working with Data from External Sources
  • Reading data from files (TXT, CSV, Excel, JSON etc.)
  • Writing data to desired file format
  • Creating Connections to Databases
  • Importing/Exporting data from/to RDBMS (My SQL/ SQLite)
  • Extracting data from Websites/API


  • Introduction to Data Visualization
  • Importance of data visualization
  • Working with Python visualization libraries
  • Creating plots using Matplotlib (Scatter Plots, Line Plots, Bar Charts, Pie Charts, Histograms)
  • Finding data distribution using density plot
  • Finding outlier using Box plotFinding correlation matrix using Heat maps
  • Data visualization Using Seaborn (Advanced visualization package)

Database Connection(Analytics in RDBMS)

  • Introduction to SQLite database file
  • Installing the SQLite DB and importing python libraries
  • Connecting SQLite/My SQL DB via python
  • Create Cursor to run SQL queries
  • Write and run the DDL statements in SQL via python
  • Fire the SQL DML statements(Insert, Update, Delete)
  • Fetch and merge data from different tables (Joins)
  • Extract the data, modify records and load it back into the DB


Mark Steele

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