Data Science & Big Data Analytics

GSFEN Tutor

January 10, 2026

Data Science & Big Data Analytics

0 Enrolled

Technology & IT

Unlock the power of data. This course is your comprehensive guide to the world of Data Science, from data collection and cleaning to statistical analysis, visualization, and predictive modeling. You will work with powerful Python libraries and learn the methodologies that data scientists at top companies use to derive insights from vast datasets. We will also explore the fundamentals of Big Data technologies and how to approach analytics at scale.

What Will I Learn?

Module 1: The Data Science Pipeline & Python Libraries
Module 2: Data Wrangling & Exploratory Data Analysis (EDA)
Module 3: Statistical Inference & Machine Learning for Data Science

Course Content

Module 1: The Data Science Pipeline & Python Libraries

Lesson 1: The Anatomy of a Data Science Project
Lesson 1.1: Business & Data Understanding (CRISP-DM Phases 1 & 2)
Lesson 1.2: The Analyst’s Toolkit – Pandas for Initial Exploration
Lesson 1 Quiz
Lesson 2.1: The Art of Data Preparation (CRISP-DM Phase 3)
Lesson 2.2: High-Performance Data Wrangling with NumPy and Pandas
Lesson 2 Quiz
Lesson 3.1: Uncovering Insights and Crafting Features
Lesson 3.2: Visual Storytelling with Matplotlib and Seaborn
Lesson 3 Quiz
Lesson 4.1: Building and Validating Predictive Models (CRISP-DM
Lesson 4.2: Your First Models with Scikit-Learn
Lesson 4.3: From Model to Impact – Deployment and Monitoring (CRISP-DM Phase 6)
Lesson 4 Quiz
References

Module 2: Data Wrangling & Exploratory Data Analysis (EDA)

Lesson 1: The Foundation: From Raw Data to Actionable Insights
Lesson 1.1 What is Data Wrangling? The Art of Preparing Your Data
Lesson 1.2: The Six-Step Data Wrangling Process: A Systematic Approach
Lesson 1.3 Data Wrangling vs. Data Cleaning: A Crucial Distinction
Lesson 1.4 Introduction to Exploratory Data Analysis (EDA): Asking Questions of Your Data
Lesson 1.5 The Goals and Importance of EDA in the Data Science Lifecycle
Lesson 1 Quiz
Lesson 2: The Data Wrangler’s Toolkit: Essential Pandas Operations
Lesson 2.1 Handling Missing Data: The First Hurdle in Data Cleaning
Lesson 2.2: Managing Duplicate Data: Ensuring Unique Observations
Lesson 2.3: Correcting Data Types: The Foundation of Accurate Analysis
Lesson 2.4 Cleaning Text and String Data: Taming Unstructured Text
Lesson 2 Quiz
Lesson 3: Advanced Data Reshaping with Pandas
Lesson 3.1 From Wide to Long: The Power of .melt()
Lesson 3.2: Summarizing with .pivot_table()
Lesson 3.3 Frequency Analysis with .crosstab()
Lesson 3.4 Combining Datasets: An Introduction to .merge()
Lesson 3 Quiz
Lesson 4: Visualizing the Story: Exploratory Data Analysis in Practice
Lesson 4.1 The Grammar of Graphics: Introduction to Seaborn
Lesson 4.2 Univariate Analysis: Understanding Distributions
Lesson 4.3 Bivariate Analysis: Uncovering Relationships
Lesson 4.4: Analyzing Distributions Across Categories & Detecting Outliers
Lesson 4 Quiz
References

Module 3: Statistical Inference & Machine Learning for Data Science

Introduction: The Symbiotic Relationship Between Statistics and Machine Learning
Lesson 1: The Foundations of Statistical Inference
Lesson 1.1 The Central Limit Theorem (CLT): The Cornerstone of Inference
Lesson 1.2 Estimation with Confidence: Beyond the Point Estimate
Lesson 1.3 The Framework of Hypothesis Testing
Lesson 1.4 Practical Statistical Tests in Python with scipy.stats
Lesson 1 Quiz
Lesson 2: The Data Science Workflow and Exploratory Data Analysis (EDA)
Lesson 2.1 A Structured Approach: The CRISP-DM Framework
Lesson 2.2: Data Wrangling and Cleaning with Pandas
Lesson 2.3: Uncovering Insights with Exploratory Data Analysis (EDA)
Lesson 2 Quiz
Lesson 3: Supervised Learning: Regression and Classification
Lesson 3.1 An Introduction to Supervised Learning
Lesson 3.2 Predicting Continuous Values: Regression
Lesson 3.3 Evaluating Regression Model Performance
Lesson 3.4 Predicting Categories: Classification
Lesson 3.5 Evaluating Classification Model Performance
Lesson 3 Quiz
Lesson 4: Unsupervised Learning and Advanced Topics
4.1 Finding Structure in Unlabeled Data: Clustering
Lesson 4.2: The K-Means Algorithm
Lesson 4.3 Determining the Optimal Number of Clusters: The Elbow Method
Lesson 4.4 From Model to Production: Deployment and Monitoring
Lesson 4 Quiz
References

About the instructor

GSFEN Tutor

4.00 (18 ratings)

27 Courses

0 students

Durations:

Lectures: 55

Students: Max 0

Level: All Levels

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Audience

Analysts, Programmers, Business Intelligence Professionals, Statisticians.

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Data Science & Big Data Analytics

Data Science & Big Data Analytics

What Will I Learn?

Course Content

Module 1: The Data Science Pipeline & Python Libraries

Lesson 1: The Anatomy of a Data Science Project

Lesson 1.1: Business & Data Understanding (CRISP-DM Phases 1 & 2)

Lesson 1.2: The Analyst’s Toolkit – Pandas for Initial Exploration

Lesson 1 Quiz

Lesson 2.1: The Art of Data Preparation (CRISP-DM Phase 3)

Lesson 2.2: High-Performance Data Wrangling with NumPy and Pandas

Lesson 2 Quiz

Lesson 3.1: Uncovering Insights and Crafting Features

Lesson 3.2: Visual Storytelling with Matplotlib and Seaborn

Lesson 3 Quiz

Lesson 4.1: Building and Validating Predictive Models (CRISP-DM

Lesson 4.2: Your First Models with Scikit-Learn

Lesson 4.3: From Model to Impact – Deployment and Monitoring (CRISP-DM Phase 6)

Lesson 4 Quiz

References

Module 2: Data Wrangling & Exploratory Data Analysis (EDA)

Lesson 1: The Foundation: From Raw Data to Actionable Insights

Lesson 1.1 What is Data Wrangling? The Art of Preparing Your Data

Lesson 1.2: The Six-Step Data Wrangling Process: A Systematic Approach

Lesson 1.3 Data Wrangling vs. Data Cleaning: A Crucial Distinction

Lesson 1.4 Introduction to Exploratory Data Analysis (EDA): Asking Questions of Your Data

Lesson 1.5 The Goals and Importance of EDA in the Data Science Lifecycle

Lesson 1 Quiz

Lesson 2: The Data Wrangler’s Toolkit: Essential Pandas Operations

Lesson 2.1 Handling Missing Data: The First Hurdle in Data Cleaning

Lesson 2.2: Managing Duplicate Data: Ensuring Unique Observations

Lesson 2.3: Correcting Data Types: The Foundation of Accurate Analysis

Lesson 2.4 Cleaning Text and String Data: Taming Unstructured Text

Lesson 2 Quiz

Lesson 3: Advanced Data Reshaping with Pandas

Lesson 3.1 From Wide to Long: The Power of .melt()

Lesson 3.2: Summarizing with .pivot_table()

Lesson 3.3 Frequency Analysis with .crosstab()

Lesson 3.4 Combining Datasets: An Introduction to .merge()

Lesson 3 Quiz

Lesson 4: Visualizing the Story: Exploratory Data Analysis in Practice

Lesson 4.1 The Grammar of Graphics: Introduction to Seaborn

Lesson 4.2 Univariate Analysis: Understanding Distributions

Lesson 4.3 Bivariate Analysis: Uncovering Relationships

Lesson 4.4: Analyzing Distributions Across Categories & Detecting Outliers

Lesson 4 Quiz

References

Module 3: Statistical Inference & Machine Learning for Data Science

Introduction: The Symbiotic Relationship Between Statistics and Machine Learning

Lesson 1: The Foundations of Statistical Inference

Lesson 1.1 The Central Limit Theorem (CLT): The Cornerstone of Inference

Lesson 1.2 Estimation with Confidence: Beyond the Point Estimate

Lesson 1.3 The Framework of Hypothesis Testing

Lesson 1.4 Practical Statistical Tests in Python with scipy.stats

Lesson 1 Quiz

Lesson 2: The Data Science Workflow and Exploratory Data Analysis (EDA)

Lesson 2.1 A Structured Approach: The CRISP-DM Framework

Lesson 2.2: Data Wrangling and Cleaning with Pandas

Lesson 2.3: Uncovering Insights with Exploratory Data Analysis (EDA)

Lesson 2 Quiz

Lesson 3: Supervised Learning: Regression and Classification

Lesson 3.1 An Introduction to Supervised Learning

Lesson 3.2 Predicting Continuous Values: Regression

Lesson 3.3 Evaluating Regression Model Performance

Lesson 3.4 Predicting Categories: Classification

Lesson 3.5 Evaluating Classification Model Performance

Lesson 3 Quiz

Lesson 4: Unsupervised Learning and Advanced Topics

4.1 Finding Structure in Unlabeled Data: Clustering

Lesson 4.2: The K-Means Algorithm

Lesson 4.3 Determining the Optimal Number of Clusters: The Elbow Method

Lesson 4.4 From Model to Production: Deployment and Monitoring

Lesson 4 Quiz

References

About the instructor

GSFEN Tutor

Audience

GSFEN is incorporated in England and Wales with Registration Number 15054938 and is registered with the Information Commissioner’s Office (ICO) under Data Protection Registration Number ZB942633