Data Science
Data science course provides an in-depth understanding of the methods and technologies used to analyze and interpret complex data. It typically begins with foundational concepts such as statistics, probability, and data manipulation using tools like Python or R. Students learn about data wrangling, cleaning, and preprocessing techniques essential for preparing data for analysis. The curriculum covers machine learning algorithms, including supervised and unsupervised learning, and introduces key concepts like neural networks and deep learning. Additionally, the course explores data visualization techniques using tools like Matplotlib, Seaborn, and Tableau to present insights effectively. Advanced topics might include natural language processing (NLP), big data technologies such as Hadoop and Spark, and deploying models into production. Practical projects and case studies are integral, providing hands-on experience in solving real-world problems. By the end of the course, students are equipped with the skills to extract meaningful insights from data, build predictive models, and make data-driven decisions in various industries.
Duration - 6 Months
Overview of Data Science
Definition and Importance
Data Science vs. Data Analytics
Applications of Data Science in Various Industries
Setting Up the Environment
Installing Python
Introduction to Jupyter Notebooks
Installing Required Libraries (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn)
Basic Python Syntax
Variables and Data Types
Basic Operators
Conditional Statements
Loops
Python Data Structures
Lists
Tuples
Dictionaries
Sets
Functions and Modules
Defining and Calling Functions
Importing Modules
Using Built-in Functions and Libraries
Data Collection
Importing Data from CSV, Excel, and SQL Databases
Web Scraping Basics with BeautifulSoup and Scrapy
Using APIs for Data Collection (requests)
Data Wrangling
Handling Missing Values
Data Cleaning Techniques
Data Transformation and Normalization
Combining and Merging Datasets
Introduction to EDA
Importance of EDA
Steps in EDA
Descriptive Statistics
Measures of Central Tendency (Mean, Median, Mode)
Measures of Dispersion (Standard Deviation, Variance, Range)
Skewness and Kurtosis
Data Visualization
Introduction to Matplotlib and Seaborn
Creating Basic Plots (Line, Bar, Histogram, Box Plot)
Customizing Plots (Titles, Labels, Legends)
Introduction to Pandas
Series and DataFrame Basics
Importing and Exporting Data
Data Manipulation
Filtering and Sorting Data
Grouping and Aggregation
Handling Time Series Data
Applying Functions to DataFrames
Introduction to NumPy
Arrays and Matrices
Basic Operations on Arrays
Advanced NumPy Techniques
Broadcasting
Vectorization
Linear Algebra Operations
Advanced Data Visualization
Heatmaps
Pair Plots
Violin Plots
Faceting with Seaborn
Interactive Visualizations
Using Plotly for Interactive Charts
Creating Dashboards with Plotly Dash
Introduction to Statistics
Probability Theory
Random Variables and Probability Distributions
Hypothesis Testing
Null and Alternative Hypotheses
Types of Tests (T-test, Chi-square test)
P-values and Significance Levels
Overview of Machine Learning
Supervised vs. Unsupervised Learning
Common Machine Learning Algorithms
Implementing Machine Learning Models
Data Preprocessing for Machine Learning
Training and Testing Models
Model Evaluation Metrics
Supervised Learning
Linear Regression
Logistic Regression
Decision Trees
Random Forests
Support Vector Machines (SVM)
Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)
Introduction to NLP
Text Preprocessing (Tokenization, Lemmatization, Stopwords Removal)
Bag of Words and TF-IDF
NLP with NLTK and SpaCy
Sentiment Analysis
Named Entity Recognition (NER)
Text Classification
Introduction to Time Series
Components of Time Series Data
Moving Averages and Smoothing
Advanced Time Series Techniques
Autoregressive Integrated Moving Average (ARIMA)
Seasonal Decomposition
Forecasting
Introduction to Deep Learning
Understanding Neural Networks
Overview of Deep Learning Frameworks (TensorFlow, Keras, PyTorch)
Building Neural Networks
Creating a Simple Neural Network with Keras
Training and Evaluating Neural Networks
Introduction to Big Data
Definition and Characteristics of Big Data
Big Data Technologies
Working with Hadoop
Hadoop Ecosystem Overview
HDFS (Hadoop Distributed File System)
MapReduce Basics
Practice Exercises for Each Module
Real-world Problem Solving
Analyzing Real-world Datasets
Interpreting Results and Drawing Conclusions
Building Small Data Science Applications
Implementing Data Processing Pipelines
Comprehensive Project Covering Multiple Modules
Real-world Problem Solving and Implementation