Course Overview:
In today's data-centric world, professionals across industries must harness statistical techniques to analyze large datasets, derive insights, and make informed business decisions. This course bridges the gap between theoretical statistics and practical application, empowering you to confidently analyze data, solve complex problems, and improve organizational outcomes. Whether you're an analyst, data scientist, or business professional, this course will help you improve your analytical skills, increase your value in your organization, and keep up with the demands of the modern job market.
This training course offers participants an in-depth understanding of fundamental statistical concepts, methodologies, and techniques used in data science. It focuses on practical applications of descriptive and inferential statistics, hypothesis testing, regression analysis, and statistical modeling. The course also emphasizes the use of statistical software such as R, Python, or Excel to implement data-driven projects, preparing participants for real-world challenges in data analysis.
Duration
10 Days
Who Should Attend
- Aspiring data scientists, data analysts, and statisticians
- Business professionals and managers involved in decision-making based on data insights
- Professionals from fields like marketing, finance, and healthcare that rely on data-driven strategies
- Students or individuals looking to enhance their statistical skills for data science
Course Objectives
By the end of this course, participants will be able to:
- Understand key statistical concepts and their application in data science.
- Use statistical software (R, Python, or Excel) to perform data analysis.
- Apply descriptive statistics to summarize and interpret data.
- Perform hypothesis testing to make informed decisions based on data.
- Utilize regression models for predicting trends and analyzing relationships between variables.
- Implement statistical techniques in real-world data science projects.
- Interpret and communicate data findings effectively to stakeholders.
Course Outline:
Module 1: Introduction to Statistics and Data Science
- Overview of statistics in data science
- Types of data: qualitative and quantitative
- Measures of central tendency and variability
- Data visualization techniques
Module 2: Descriptive Statistics
- Understanding distributions (normal, skewness, kurtosis)
- Measures of dispersion: variance and standard deviation
- Correlation and covariance
- Data summarization and presentation
Module 3: Probability and Hypothesis Testing
- Probability theory and distributions (binomial, Poisson, etc.)
- Sampling methods and the law of large numbers
- Null and alternative hypotheses
- T-tests, chi-square tests, and p-values
Module 4: Regression Analysis and Predictive Modeling
- Simple linear regression
- Multiple linear regression
- Model evaluation metrics (R-squared, RMSE)
- Overfitting and model validation techniques
Module 5: Advanced Statistical Methods and Applications
- ANOVA (Analysis of Variance)
- Logistic regression for classification problems
- Time series analysis
- Hands-on project: solving real-world data science problems
