Course: Data Science for Business

Our program is designed for professionals eager to harness the power of data to drive innovation and informed decision-making in various business domains. Engage in hands-on sessions that apply machine learning techniques to real-world business use cases, such as predicting customer churn, sales forecasting, and fraud detection. Join us and bridge the gap between data and insightful business decisions.

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Course Outcomes

Upon completion of this course, participants will

  • Develop the capability to make informed business decisions using data-driven insights.
  • Grasp essential statistical concepts for business analysis and apply inferential statistics in various contexts.
  • Develop advanced visualization techniques for gaining insights across diverse sectors.
  • Apply machine learning techniques to real business use cases, such as customer churn prediction, sales forecasting, and fraud detection.
  • Master forecasting techniques for financial and non-financial data, including models such as AR, MA, and ARIMA.


Who It's For

This course is ideal for:


  • Business Professionals
  • Finance professionals
  • Marketing and Sales professionals
  • Entrepreneurs and Decision Makers
  • Students and Researchers
  • Anyone Interested in Data Analysis


Prerequisites

  • No prior Python experience required. This course is designed for absolute beginners in Python and programming. No prior coding experience is necessary.
  • Participants should have access to a computer during all training sessions.





Course Modules

1. Python for Data Science

  • Master the fundamentals of Python programming tailored for data science applications.

4. Statistics Essentials

  • Grasp essential statistical concepts for data analysis.
  • Apply inferential statistics in a financial context.

7. Machine Learning in Finance

  • Understand the basics of linear regression in financial modeling.
  • Apply regression analysis to financial datasets.

2. Overview of Data Types

  • Understand the types and characteristics of data.
  • Learn to use data science tools for efficient financial data handling.

5. Data Cleaning and Preprocessing:

  • Learn techniques to identify and handle issues such as missing data outliers, and inconsistency
  • Normalize and scale financial data for analysis.

8. Time Series Analysis

  • Conduct exploratory data analysis for key financial insights.
  • Dive into time series analysis to identify trends and seasonality.

3. Data Collecting and Manipulating

  • Acquire skills in collecting data using APIs , CSVs, and web scraping.
  • Manipulate data effectively with Pandas library.

6. Data Visualization

  • Create meaningful visualizations using Matplotlib and Seaborn.
  • Develop advanced visualization techniques for financial insights.

9. Financial Forecasting

  • Master forecasting techniques for financial data.
  • Build and evaluate forecasting models  such as AR, MA, and ARIMA.