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40 Hours

Python for Data Analytics Diploma

"Unlock the Power of Data, Code Your Way to Success !"

Program Summary:

This intensive program provides comprehensive training in data analysis and visualization, with a focus on complex data problems and business applications. Participants will learn Python for tasks such as data collection, cleaning, preparation, and analysis of time-series data. The course covers various data visualization methods and essential tasks like data wrangling and descriptive statistics. The final module delves deeper into time-series data analysis and teaches participants to use linear regression models to predict unknown and future values.

IGS&PartnerLEARNING OBJECTIVES

By the end of this program, the participants will be able to:

  1. Learn Python – the most popular programming language and for Data analysis.
  2. Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes.
  3. Apply Python fundamentals, Python data structures, and working with data in Python.
  4. Demonstrate proficiency in using Python libraries such as Pandas & NumPy and developing.
  5. Play the role of a Data Analyst working on a real project.
  6. Develop Python code for cleaning and MI preparing data for analysis – including handling missing values, formatting, normalizing, and binning data.
  7. Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas and NumPy.
  8. Manipulate data using data frames, summarize data, understand data distribution and perform correlation.
  9. Implement data visualization techniques and plots using Python libraries, such as Matplotlib and Seaborne.
  10. Create different types of charts and plots such as line, area, histograms, bar, pie, box and scatter.
  11. Build and evaluate regression models using machine learning and use them for prediction and decision making.
  12. Apply your skills to perform data collection, exploratory data analysis, data visualization model development, and model evaluation using machine learning models (regression models).