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Data Science

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Course Highlights:

Python Programming: Master Python and key libraries like NumPy and Pandas.
Data Analysis & Visualization: Learn techniques using Matplotlib and Seaborn.
Statistics & Machine Learning: Understand statistical concepts and machine learning algorithms with Scikit-Learn.
AI & Deep Learning: Explore neural networks with TensorFlow and Keras.
Practical Projects and Case Studies: Apply your skills in real-world projects and a capstone project.
Career Preparation: Get job-ready with resume building, interview prep, and placement assistance.

Let's talk about Data Science....

Before we dive into data science, I have a question. Have you ever wondered how Netflix recommends movies you might like or how Spotify creates playlists that match your mood? The answer lies in a powerful field called Data Science.

Data science is all about extracting knowledge and insights from data. It's like having a magic decoder ring that unlocks the hidden messages within the vast amounts of information we generate every day.

So, what does a data scientist do?

Think of a data scientist as a detective who solves puzzles using data as clues. Here's a typical process:

Ask a Question: It all starts with a question. Businesses might want to know how to improve customer satisfaction, predict sales trends, or identify potential fraud. Researchers might be looking for patterns in climate data or disease outbreaks.
Collect Data: Once a question is defined, data scientists need to gather relevant information. This could involve scraping data from websites, accessing databases, or conducting surveys.
Clean and Prepare Data: Raw data is often messy and incomplete. Data scientists spend a significant amount of time cleaning the data, removing errors, and formatting it for analysis.
Analyze the Data: Here's where the magic happens! Data scientists use various tools and techniques to analyze the data. This might involve statistical analysis, machine learning algorithms, or data visualization.
Interpret the Results: Once analyzed, the data needs interpretation. Data scientists need to explain what the results mean, identify patterns, and draw conclusions.
Communicate Insights: Finally, data scientists need to communicate their findings in a clear and concise way. This could involve creating reports, dashboards, or presentations for stakeholders.

Why is data science so important?

Data science is revolutionizing how we live and work. Here are a few reasons why it's so important:

Better Decision Making: Data-driven insights enable businesses and organizations to make informed decisions with greater confidence.
Improved Efficiency: Data science can be used to automate tasks, streamline processes, and optimize operations.
Personalized Experiences: From targeted advertising to customized product recommendations, data science helps companies tailor offerings to individual users.
Scientific Discoveries: Data science plays a crucial role in scientific research, enabling researchers to analyze complex datasets and make groundbreaking discoveries.

Is data science a good career choice?

The demand for skilled data scientists is rapidly growing across industries. It's a challenging but rewarding career path with excellent job prospects and good earning potential.

How can I get started with data science?

If you're curious about data science, here are some steps you can take:

Learn the Basics: Start by taking online courses or reading introductory books on data science, statistics, and programming languages like Python.
Practice with Code: Many online platforms offer free data sets and tutorials to practice your coding skills in data analysis and visualization.
Participate in Online Communities: Join online forums and communities for data science enthusiasts. Here you can ask questions, share your work, and learn from others.
Contribute to Open Source Projects: There are many open-source data science projects available online. Contributing to such projects is a great way to gain practical experience.

The world of data science is vast and ever-evolving. But by taking those first steps, you can unlock its potential and use data to make a difference in your field.

Course Overview

Module 1: Introduction to Data Science

Overview of Data Science

Defination
Evolution
Scope

Module 2: Steps of Data Science

There are 6 steps of Data Science Process

Module 3: Importance of Data Science

There are various industries in which data science is been used. The most popular once are:

Healthcare
Finance
Marketing
Agriculture
Sports

Module 4: Python Programming for Data Science

Basics of Python Programming

Python as a General Purpose Language
Python Syntax and Structure
Python Comments
Application of Python

Module 5: Data types and Variables

Data types determine the kind of value a variable can hold (e.g., int, float, str), while Variables are symbolic names assigned to these values to store and manipulate data in the program.

Data Types:- Int , Float , Str , Bool , Complex
Variables:- Declaration , Naming convention , concept of valid invalid.

Module 6: Operators

There are 7 types of Operators in Python which are

Module 7: Control Flow Statements

Control statements manage the flow of execution:

Module 8: String

String Manipulation

String Indexing
String Slicing
String Concatenation
String Formatting
Common String Method

Module 9: Array

Array in Python

Introduction to Array
Array Operation
Array Slicing

Module 10: List

A list in Python is an ordered, mutable collection of items (e.g., [1, 2, 3, "apple"]).

Versatile List Data Structure
List Indexing and Slicing
List Comprehension
List Slicing
Common List Operations

Module 11: Tuples

A tuple in Python is an ordered, immutable collection of items (e.g., (1, 2, 3, "apple")).

Immutable Nature of Tuples
Use Cases for Tuples

Module 12: Sets

A set in Python is an unordered collection of unique items (e.g., {1, 2, 3, "apple"}).

Uniqueness Property of Sets
Application in Solving Distinct Element Problems

Module 13: Dictionaries

A dictionary in Python is an ordered collection of key-value pairs (e.g., {"name": "Alice", "age": 25})

Key-Value Pairs
Dictionary Manipulation
Role in Efficient Data Storage and Retrieval

Module 14: Functions and lambda

Meaning of Functions and Lambda

Functions: Defined using def, perform reusable tasks (e.g., def add(a, b): return a + b).
Lambda: Anonymous functions for simple tasks (e.g., add = lambda a, b: a + b).

Module 15: Exception Handling

Exception handling in Python uses try, except, else, and finally blocks to manage errors and ensure graceful error recovery (e.g., try: ... except Exception as e: ... finally: ...).

Introduction to Exceptions
Try and Except Blocks
Finally Block
Else block

Module 16: NumPy Library

The NumPy library provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays (e.g., import numpy as np).

Introduction to NumPy
Arrays in NumPy
Array Indexing and Slicing
Random Module in NumPy

Module 17: Pandas Library

The pandas library is a powerful open-source data manipulation and analysis tool built on top of the Python programming language, providing data structures like DataFrames and Series for efficiently handling and analyzing structured data.

Module 18: Matplotlib Library

A Python library used for creating static, interactive, and animated visualizations in a wide variety of formats and interactive environments.

Introduction to Matplotlib
Bar Charts and Histograms
Box plot
Pi chart
Scatterplot

Module 19: Seaborn library

Seaborn is a powerful Python visualization library based on Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics.

Module 20: Descriptive statistics

Descriptive statistics summarize and describe the main features of a dataset using measures like mean, median, mode, and standard deviation.

Definition of Descriptive Statistics
Measures of Central Tendency: Mean, Median and Mode
Measures of Dispersion : Range, Variance and Standard deviation
Quartiles and Percentiles
Interquartile Range (IQR)
Outliers
Summary Statistics

Module 21: Inferential statistics

Inferential statistics make predictions or inferences about a population based on sample data using techniques like hypothesis testing and confidence intervals.

Definition of Inferential Statistics
Sampling Techniques: Random Sampling and Stratified Sampling
Population and Sample
Sampling Distributions

Module 22: Hypothesis testing

Hypothesis testing evaluates evidence from sample data to make decisions about a population, typically using null and alternative hypotheses.

Hypothesis Testing
Null Hypothesis and Alternative Hypothesis
Type I and Type II Errors

Module 23: Introduction to Machine Learning

Machine learning enables computers to learn from data, identify patterns, and make decisions with minimal human intervention.

Definition of Machine Learning
Key Concepts
Applications of Machine Learning

Module 24: Types of Machine Learning:

Machine learning types: supervised (labeled data), unsupervised (unlabeled data), and reinforcement (reward-based learning).

Supervised Learning
Unsupervised Learning
Reinforcement Learning

Module 25: Model Training and Evaluation

Model training fits data to algorithms; evaluation assesses performance using metrics like accuracy, precision, recall, and cross-validation.

Model Training Process
Model Evaluation
Cross-Validation
Overfitting and Underfitting

Module 26 : Feature Engineering and Selection

Feature engineering creates new input variables from raw data; feature selection chooses the most relevant variables for model performance.

Feature Selection
Dimensionality Reduction
Handling Categorical Variables

Module 27: Supervised Learning Algorithms

Supervised learning algorithms predict outcomes from labeled data using methods like linear regression, decision trees, and support vector machines.

Module 28: Unsupervised Learning Algorithms

Unsupervised learning algorithms find patterns in data without labels, including clustering, dimensionality reduction, and association rule learning.

Clustering algorithms (K-means, Hierarchical)
Dimensionality reduction techniques (PCA)

Module 29: Data Visualization with Power BI

Data Visualization with Power BI transforms raw data into interactive, insightful reports and dashboards for better decision-making and data analysis.

Introduction to Power BI
Connecting to data
Creating interactive dashboards
Power BI for reporting and analysis

Module 30: Projects

- Real-world Data Science project incorporating Python, statistics, machine learning, Flask, and Power BI. - Students will work on a comprehensive project to apply their skills and showcase their understanding of the entire data science workflow. - Project Topics :-