Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Video and Text Based |
Course Overview
The Statistics for Managers Using Python course is being offered by the Indian Institute of Management Tiruchirappalli (IIM Trichy). This Statistics for Managers Using Python course is being designed for managers and business leaders aiming to harness the power of data driven decision making. This three-day intensive course provides participants with the statistical tools and python programming skills necessary to analyze business data effectively.
The participants will engage in interactive lectures, hands-on workshops, and real-world case studies, ensuring a practical understanding of concepts. This Statistics for Managers Using Python course is structured to start from the basics, making it accessible even to those without prior programming experience. The participants will be equipped to transform data into actionable intelligence, enhancing their leadership and decision-making capabilities.
The Highlights
- 3-day programme
- Led by experienced IIM Trichy faculty
- Case studies
- Access to IIM Trichy's campus facilities
- Comprehensive course materials provided
- Certificate of completion from IIM Trichy
Programme Offerings
- Case Studies
- peer learning
- Real-world applications
- IIM Trichy faculty
- Certificate from IIM Trichy
- Comprehensive curriculum
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIM Trichy |
The fees for the Statistics for Managers Using Python course is :
Fees components | Amount |
Programme fees | Rs. 39,000/- + GST |
What you will learn
The participants will delve into the fundamentals of Python programming, focusing on its application in data analysis. This Statistics for Managers Using Python course covers essential statistical concepts, including descriptive statistics, probability distributions, hypothesis testing, and regression analysis. Through hands-on sessions, the participants will manipulate datasets using Python libraries like Pandas and visualize data using tools such as Matplotlib and Seaborn.
By integrating statistical theory with practical Python applications, the participants will gain the skills to analyze complex business data, draw meaningful insights, and make informed decisions. The curriculum is designed to build confidence in interpreting data and presenting findings effectively to stakeholders.
Who it is for
The target audience for the Statistics for Managers Using Python course includes:
The Syllabus
Session 1: Introduction to Python for Data Analysis
- Overview of Python as a tool for data analysis
- Setting up the Python environment with Anaconda and Jupyter Notebooks
- Basic Python syntax, data types, and operations for beginners
Session 2: Managing Data with Python
- Introduction to Pandas for Data Manipulation
- Importing and cleaning data from various sources
- Basic operations: filtering, sorting, and summarizing data
Session 3: Basics of Descriptive Statistics
- Understanding measures of central tendency (mean, median, mode)
- Measures of dispersion: range, variance, standard deviation
- Visualization techniques for descriptive statistics
Session 4: Data Visualization Fundamentals
- Introduction to Python libraries for data visualization
- Creating histograms, box plots, and scatter plots
- Best practices for visual data analysis
Session 1: Probability and Distributions
- Basics of probability theory
- Normal distributions and z-scores
- Other significant distributions (Binomial, Poisson)
Session 2: Inferential Statistics: Estimation and Hypothesis Testing
- Sampling and the Central Limit Theorem
- Constructing and interpreting confidence intervals
- Introduction to hypothesis testing: significance, p-value, and test types
Session 3: Comparing Groups and ANOVA
- T-tests for comparing means (independent samples and paired samples)
- Analysis of Variance (ANOVA) for comparing more than two groups
- Practical Exercises in Python
Session 4: Correlation and Regression Analysis
- Understanding correlation: Pearson and Spearman coefficients
- Basics of linear regression: fitting a model, interpreting coefficients
- Visualizing regression models with Python
Session 1: Multivariate Statistics
- Introduction to multivariate analysis techniques
- Multiple regression analysis: exploring complex relationships
- Practical implementation in Python
Session 2: Time Series Analysis Basics
- Components of time series data: trend, seasonality, cyclicity, and irregular components
- Techniques for decomposing time series
- Simple forecasting methods: moving averages, exponential smoothing
Session 3: Advanced Data Visualization Techniques
- Advanced plotting with Python libraries
- Creating interactive plots
- Visual storytelling with data
Session 4: Integrating Statistics into Business Strategy
- Frameworks for data-driven decision making
- Case studies: leveraging statistical analysis for strategic advantages
- Ethical considerations in data analysis