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Statistics for Data Science and Business Analysis
Enhance your business data insights with our Statistics for Data Science course. Equip your teams with practical skills to improve decision-making. Sign up now!
Program Overview
In a world where 402.7 million terabytes of data are generated daily, the true value lies in uncovering actionable insights through data science, regardless of the size of your business. Whether you work in marketing, sales, or product development at a startup, small business, or large enterprise, identifying hidden patterns in your data can lead to smarter decisions and create personalized customer experiences.
However, making data science both approachable and impactful requires a solid understanding of statistics. Statistical techniques simplify the process of extracting meaningful insights from data, enabling you to make informed decisions with confidence. To support this, Uptut offers a comprehensive training program: Statistics for Data Science and Business Analysis, designed to equip you with the essential statistical knowledge needed to fully leverage your business data.
This 100% live training program will provide learners with a deep understanding of statistics, covering everything from basic concepts to advanced statistical modeling, all with a focus on practical business applications.
Since we start with the fundamentals, this program is suitable for everyone, regardless of their prior statistical knowledge. Team leaders will learn how statistics can enhance decision-making through valuable insights, while other team members will improve their skills in applying advanced modeling techniques to generate those insights.
To learn more about the course, contact us today.
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Training Objectives
- Understand the fundamentals of statistics and different types of data
- Master plotting various data types and visualizing data effectively
- Calculate central tendency, asymmetry, variability, correlation, and covariance measures
- Distinguish between different types of distributions and estimate confidence intervals
- Perform hypothesis testing
- Grasp the mechanics of regression analysis
- Use and interpret dummy variables in regression models
- Apply statistical concepts in data science with practical applications using Python and R
Key training modules
- Introduction
- A brief overview of the course
- Sample or Population Data
- Understanding the difference
- The Fundamentals of Descriptive Statistics
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- Types of data and levels of measurement
- Categorical variables and corresponding visualization techniques
- Numerical variables and frequency distribution tables
- Histogram charts
- Cross tables
- Scatter plots
- Measures of Central Tendency, Asymmetry, and Variability
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- Fundamentals of central tendency measures – mean, median, and mode
- Measuring skewness
- Measuring data spread through standard deviation and variance
- Analyzing relationships between variables through covariance and correlation
- Distributions
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- Fundamentals of inferential statistics
- What is a distribution
- Normal and Standard Normal Distribution
- The Central Limit Theorem
- Standard Error
- Estimators and Estimates
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- Basics of estimators and estimates
- Confidence intervals and their calculation with known population variance
- Student’s T Distribution
- Using Student’s T-score to calculate confidence intervals with unknown population variance
- The Margin of Error and its significance
- Confidence Intervals: Advanced Topics
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- Confidence intervals for two means with dependent samples
- Confidence intervals for two means with independent samples
- Hypothesis Testing: Introduction
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- Null and alternative hypotheses
- Rejection region and significance level
- Type 1 Error vs Type II Error
- Hypothesis Testing: Let’s Start Testing!
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- P-value
- Testing for the mean with known and unknown population variance
- Testing for the mean with dependent and independent samples
- The Fundamentals of Regression Analysis
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- Fundamentals of regression analysis
- Relationship between correlation and causation
- The Linear Regression Model (LRM)
- Correlation vs Regression
- Geometrical representation of LRM
- Subtleties of Regression Analysis
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- R-squared and its utility
- The Ordinary Least Squares (OLS) method
- Regression tables
- Adjusted R-squared
- F-statistic and its utility
- Assumptions for Linear Regression Analysis
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- Assumptions of the Ordinary Least Squares method
- Linearity
- No endogeneity
- Normality and homoscedasticity
- No autocorrelation
- No multicollinearity
- Dealing with Categorical Data
- Dummy variables
- Bonus Lecture
- What’s next
