Statistics for Data Science and Business Analysis

Transform your business data into insights with our Statistics for Data Science course. Train your teams with practical skills to enhance decision-making. Sign up now!

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Program Overview

In a world where 402.7 million terabytes of data are generated daily, regardless of the business size, the true value lies in uncovering actionable insights through data sciences. Whether you're in marketing, sales, or product development within a startup, small business, or large enterprise, hidden patterns in your data can drive smarter decisions and foster personalised customer experiences.

However, the key to making data science both approachable and impactful lies in 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 to fully leverage your business data.

Throughout this 100% live training program, learners will gain 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 basics, this program is suitable for everyone, regardless of their existing statistical knowledge. Team leaders will discover how statistics can streamline decision-making through valuable insights, while other team members will get better at 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
    1. Types of data and levels of measurement
    2. Categorical variables and corresponding visualization techniques
    3. Numerical variables and frequency distribution tables
    4. Histogram charts
    5. Cross tables
    6. Scatter plots
  • Measures of Central Tendency, Asymmetry, and Variability
    1. Fundamentals of central tendency measures - mean, median, and mode
    2. Measuring skewness
    3. Measuring data spread through standard deviation and variance
    4. Analyzing relationships between variables through covariance and correlation
  • Distributions
    1. Fundamentals of inferential statistics
    2. What is a distribution
    3. Normal and Standard Normal Distribution
    4. The Central Limit Theorem
    5. Standard Error
  • Estimators and Estimates
    1. Basics of estimators and estimates
    2. Confidence intervals and their calculation with known population variance
    3. Student’s T Distribution
    4. Using Student’s T-score to calculate confidence intervals with unknown population variance
    5. The Margin of Error and its significance
  • Confidence Intervals: Advanced Topics
    1. Confidence intervals for two means with dependent samples
    2. Confidence intervals for two means with independent samples
  • Hypothesis Testing: Introduction
    1. Null and alternative hypotheses
    2. Rejection region and significance level
    3. Type 1 Error vs Type II Error
  • Hypothesis Testing: Let’s Start Testing!
    1. P-value
    2. Testing for the mean with known and unknown population variance
    3. Testing for the mean with dependent and independent samples
  • The Fundamentals of Regression Analysis
    1. Fundamentals of regression analysis
    2. Relationship between correlation and causation
    3. The Linear Regression Model (LRM)
    4. Correlation vs Regression
    5. Geometrical representation of LRM
  • Subtleties of Regression Analysis
    1. R-squared and its utility
    2. The Ordinary Least Squares (OLS) method
    3. Regression tables
    4. Adjusted R-squared
    5. F-statistic and its utility
  • Assumptions for Linear Regression Analysis
    1. Assumptions of the Ordinary Least Squares method
    2. Linearity
    3. No endogeneity
    4. Normality and homoscedasticity
    5. No autocorrelation
    6. No multicollinearity
  • Dealing with Categorical Data
  • Dummy variables
  • Bonus Lecture
  • What’s next

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Opt-in Certifications
AWS, Scrum.org, DASA & more
100% Live
on-site/online training
Hands-on
Labs and capstone projects
Lifetime Access
to training material and sessions

How Does Personalised Training Work?

Skill-Gap Assessment

Analysing skill gap and assessing business requirements to craft a unique program

1

Personalisation

Customising curriculum and projects to prepare your team for challenges within your industry

2

Implementation

Supplementing training with consulting support to ensure implementation in real projects

3

Why statistics for your business?

  • Data-centric decision-making: Equipping your employees with statistical knowledge will eventually reduce reliance on guess-based, intuitive decision-making. It will enable them to uncover hidden patterns in raw business data, resulting in evidence-based business strategies.
  • Market analysis proficiency: Employees with statistically sound backgrounds can significantly enhance competitor and market analysis by working with complex consumer data. Insights from this analysis improve product development and marketing strategies, giving you a competitive edge even in a crowded market.
  • Operational Efficiency: Trained employees can identify and address discrepancies in your business data through efficient and prompt analysis. Over time, reduced errors lead to optimal resource allocations and operations, leading to faster project completion and significant cost savings.
  • Thorough reporting: Statistical knowledge also enhances visualization and reporting capabilities. Employees with this knowledge can furnish accurate and insightful reports, providing a granular view of your performance.

Who will Benefit from this Training?

  • Data and business analysts
  • Sales and marketing teams

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Frequently Asked Questions

1. What are the pre-requisites for this training?
Faq PlusFaq Minus

The training does not require you to have prior skills or experience. The curriculum covers basics and progresses towards advanced topics.

2. Will my team get any practical experience with this training?
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With our focus on experiential learning, we have made the training as hands-on as possible with assignments, quizzes and capstone projects, and a lab where trainees will learn by doing tasks live.

3. What is your mode of delivery - online or on-site?
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We conduct both online and on-site training sessions. You can choose any according to the convenience of your team.

4. Will trainees get certified?
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Yes, all trainees will get certificates issued by Uptut under the guidance of industry experts.

5. What do we do if we need further support after the training?
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We have an incredible team of mentors that are available for consultations in case your team needs further assistance. Our experienced team of mentors is ready to guide your team and resolve their queries to utilize the training in the best possible way. Just book a consultation to get support.

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