Course Details
Exploratory Data Analysis and Descriptive Statistics (Stats for Data Science )
Data Science
Created On
June 26, 2023Description
The application of statistical methods and techniques to analyze and interpret data in order to gain insights and make data-driven decisions. Studying EDA and statistics for data science provides the ability to understand and explore data, make informed decisions, build accurate models, and communicate actionable insights effectively.
Overview
This course is designed to equip students with the fundamental statistical concepts and data analysis techniques essential for a successful career in data science. The curriculum focuses on developing a strong foundation in statistical reasoning and practical skills to manipulate, visualize, and draw meaningful insights from data. **Provide a solid understanding of statistical principles and their application in data science. Equip students with practical skills to analyze, visualize, and interpret data. Develop the ability to make data-driven decisions and draw meaningful insights. Prepare students for more advanced data science coursework and real-world data analysis tasks**.
Features
- Assessment
- Quizzes
- Interview Questions
- Resume Preparation
- Final Project to demonstrate ability to apply statistical analysis techniques.
- On demand recorded videos
- Completion certificate
What you'll learn
- Basic understanding of Statistics
- Normal Distribution
- Continuous vs. Discrete Variables
- Central Limit Theorem
- Z-Score and Z-Tables
- Normal Distribution vs. t-Distribution
- Confidence Intervals
- Standard Deviations
- Sampling Distribution
- Hypothesis Testing for Proportions
- t-Score and t-Tables
- Importance of Statistics in the world of Data Science
Prerequisites
Curriculum
- 12 modules
Understanding the role of statistics in data science.
Basic statistical terminology and concepts.
Measures of central tendency (mean, median, mode).
Measures of dispersion (variance, standard deviation).
Data visualization techniques.
Chebyshev's Inequality
Use of Python in Statistics
Central Limit Theorem (CLT)
Introduction to hypothesis testing.
Type I and Type II errors.
p-values and significance levels.
Populations vs. samples.
Sampling techniques and their importance.
Understanding sampling distributions.
Confidence intervals.
Practical applications of hypothesis testing.
Pairplot: Scatter matrices
Implot: Univariate regression plot
Pearson Correlation Coefficient.
Linear regression.
Multiple regression for analyzing multiple factors.
Gaussian (Normal) Distribution.
Log-Normal Distribution.
Introduction to other distributions
Poisson correlation Coefficient, Binomial.
Spearman's Rank Correlation Coefficient
Comparing multiple groups.
Understanding variability within and between groups.
Principal Component Analysis (PCA).
Feature selection vs. feature extraction.
Data scaling techniques.
Standardization vs. normalization.
Finding Outliers using Z-Score and IQR
Hands-on experience with statistical software and libraries like Python, R, and pandas.
Real-world case studies and projects to apply learned concepts.
Instructors
Mr. Ravishanker, a seasoned financial leader with 33 years of experience in company accounts and practices, has held pivotal roles as Director at Majestic Colors Pvt. Ltd., Partner at Paper Corporation of India, and presently serves as the CFO at Aishwarya Publications Pvt. Ltd., Mumbai, showcasing a distinguished career marked by financial excellence and strategic acumen.
Experienced Technostragist with three decades in IT, excelling in Sales, Product Management, and Marketing across IT hardware, networking, and software. Proven strategic planner, startup pioneer, and mentor for large teams. Marketing authority in areas such as Business Incubation, Branding, and Sales. Vast industry expertise spans ITES, IT Hardware, IT Training (Software), Distribution, and Retail. Recognized for interpersonal leadership, intuitive decision-making, and a collaborative approach. Complemented by four years as a Data Scientist, enhancing analytical and problem-solving skills with a deep passion for coding and a knack for simplifying complex concepts,
- 12 Modules
- 12 Hours
- Data Science
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