# Introduction to Data Science

### Introduction

A high-level overview of key Data Science disciplines is provided in this course. Along with an overview of frequent advantages, difficulties, and adoption problems, a fundamental grasp of data science from both a commercial and technological standpoint is given.

You will master the fundamentals of data science in this course, as well as how to use Python, a potent open-source tool. You will learn about fascinating ideas including exploratory data analysis, basic statistics, testing of hypotheses, tools for regression and classification modeling, and an introduction to machine learning.

### Course Objectives

At the end of this course the participants will be able to:

• Data Science Tools & Technologies
• Statistics for Data Science
• Python for Data Science
• Exploratory Data Analysis
• Advanced Statistics & Predictive Modeling
• Optimize Model Performance
• Dimensionality Reduction
• Basics of Machine Learning

### Targeted Audience

• A beginner who is interested in data science and want to learn basic data science skills
• Those looking for a more robust, structured data science learning program
• Data Analysts, Economists, or Researchers
• Software or Data Engineers

### Unit 1: Foundation for Data Science & “Probability & Statistics”

• Introduction to Data Science
• Analytics Landscape
• Life Cycle of a Data Science Projects
• Data Science Tools & Technologies
• Measures of Central Tendency
• Measures of Dispersion
• Descriptive Statistics
• Probability Basics
• Marginal Probability
• Bayes Theorem
• Probability Distributions
• Hypothesis Testing

### Unit 2:  Basics of Python & Python Built-in Data Structures

• Install Anacond
• Data Types & Variables
• String & Regular Expressions
• Python list
• Python dictionaries
• Python set
• Python tuple
• Comprehensions

### Unit 3: “Control & Loop Statements in Python” & “Functions & Classes in Python”

• For Loop
• While Loop
• Break Statement
• Next Statements
• Repeat Statement
• if, if…else Statements
• Switch Statement
• Writing your own functions (UDF)
• Calling Python Functions
• Functions with Arguments
• Calling Python Functions by passing Arguments
• Lambda Functions
• Classes & Objects

### Unit 4: Working with Data & Analyzing Data using Pandas

• Writing files from Python
• Reading files using Pandas library
• Saving Data using Pandas library
• Clean & Prepare Datasets
• Manipulate DataFrame
• Summarize Data
• Churn Insights from Data

### Unit 5: “Visualize Data” & Advanced Statistics & Predictive Modeling

• Charts using Matplotlib
• Charts using Seaborn
• Charts using ggplot
• ANOVA
• Linear Regression (OLS)
• Case Study: Linear Regression
• Principal Component Analysis
• Factor Analysis
• Case Study: PCA/FA
• Logistic Regression (MLE)
• Case Study: Logistic Regression
• K-Nearest Neighbor Algorithm
• Case Study: K-Nearest Neighbor Algorithm
• Decision Tree
• Case Study: Decision Tree

### Unit 6: Time Series Forecasting & Introduction to Machine Learning

• Understand Time Series Data
• Visualizing TIme Series Components
• Exponential Smoothing
• Holt's Model
• Holt-Winter's Model
• ARIMA
• Case Study: Time Series Modeling on Stock Price
• What is Machine Learning?
• Supervised Learning
• Unsupervised Learning
• Using Scikit-learn
• Scikit-learn classes
• Case Study: Machine Learning Algorithm