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Abstract Blue Light
Certified Data Science Associate

Target Students​

Aspiring data scientists, analysts, and professionals looking to gain foundational knowledge in data science.

Duration :  16 hours (2 days)
Learning Objectives

-Understand core concepts and applications of data science.

-Gain practical skills in data collection, preparation, and exploratory data analysis.

-Learn basic statistical methods used in data science.

-Develop the ability to communicate data-driven insights effectively.

Exam Formats​
 

Multiple-choice

Practical Analysis Tasks

Exam Options​
 

Online

In-Person

Exam Codes: DSA-801
Exam Duration: 2 hours
Passing Score​​: 70% 
Course Outline - Foundations of Data Science
​
Module 1: Introduction to Data Science
​

  • Overview of Data Science

  • Understanding core concepts and definitions

  • The role of data science in business decision-making

  • Applications of data science in various industries

  • Data Science Lifecycle

  • Introduction to the data science process: Data collection, cleaning, analysis, and interpretation

  • Key stages of data analysis and decision-making

Module 2: Data Collection and Preparation
​

  • Data Types and Structures

  • Understanding different data types: Structured, Unstructured, and Semi-Structured data

  • Basic data structures: Tables, Arrays, and Databases

  • Data Collection Techniques

  • Methods for data collection: Surveys, Web Scraping, and APIs

  • Best practices for ensuring data quality during collection

  • Data Cleaning and Preprocessing

  • Techniques for data cleaning: Handling missing values, outliers, and duplicates

  • Data transformation: Normalization, standardization, and feature scaling

Exploratory Data Analysis and Statistical Concepts
Module 3: Exploratory Data Analysis (EDA)
'

  • Introduction to EDA

  • The importance of exploratory data analysis in data science

  • Key techniques for EDA: Summary statistics, Data visualization

  • Data Visualization Techniques

  • Introduction to data visualization tools: Matplotlib, Seaborn, and Plotly

  • Creating effective visualizations: Histograms, Boxplots, Scatter plots

  • Identifying patterns and trends through visualization

Module 4: Introduction to Statistical Analysis
​

  • Basic Statistical Concepts

  • Overview of statistics in data science: Descriptive and Inferential statistics

  • Key statistical measures: Mean, Median, Mode, Variance, Standard deviation

  • Probability Theory and Distributions

  • Introduction to probability concepts: Probability distributions, Sampling

  • Understanding common distributions: Normal, Binomial, and Poisson distributions

  • Hypothesis Testing

  • Concepts of hypothesis testing: Null and alternative hypotheses

  • Conducting basic hypothesis tests: T-tests, Chi-square tests

  • Interpreting p-values and confidence intervals

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