Data Science Training In Chandigarh

What is Data Science

• Overview of data science and its significance
• Data science lifecycle
• Key concepts and terminology

•  Introduction and Overview
•  Install and Setup Python and
• PYcharm IDE
• Variable Types and Input
•  Basic Operators
•  Decision Making
• Loops and Date & Time
• Numbers and Strings
•  Lists,Tuples and Dictionary
•  Built-in Functions,User De?ned
• Functions
• Built-In Modules
• File I/O
• Managing Directories
• Exception handling
• Python Slice and Slicing 

NumPy is a Python library for numerical computations, optimized for array operations. Its
foundational for data science due to its efficiency in handling large datasets.

Arrays, Indexing, and Slicing: NumPy arrays are multi-dimensional data structures for
storing numbers. Indexing and slicing allow accessing specific elements or subsets (e.g.,
extracting a row from a matrix).

Mathematical Operations and Broadcasting:

NumPy supports element-wise operations
(e.g., adding two arrays) and broadcasting, which applies operations to arrays of different
shapes efficiently.

Practical:

Perform matrix operations (e.g., dot product) and compute statistics (e.g., mean, standard deviation) on sample datasets to understand array manipulation.

•  Pandas is a powerful library for data manipulation and analysis, built on NumPy. Its essential
  for handling structured data like spreadsheets.
• DataFramesandSeries: DataFrames are table-like structures with rows and columns, while
  Series are single-column data structures. They enable data organization and querying.
  • Data Cleaning, Merging, and Grouping: Cleaning involves handling missing values and
  outliers. Merging combines datasets (e.g., joining sales and customer data). Grouping aggre
  gates data (e.g., sales by region).
  • Practical: Analyze a dataset (e.g., sales data) to clean missing values, merge tables, and  compute group-wise statistics, preparing for real-world data analysis.

Objectives
Learn to create insightful visualizations and query databases using SQL.

• Numpy Library InstallData visualization transforms raw data into visual insights, critical for communicating findings.
  • Plot Types: Matplotlib supports line plots (trends over time), scatter plots (relationships be
  tween variables), bar plots (comparisons), and histograms (distributions). Seaborn enhances
  these with aesthetically pleasing statistical visualizations.
  • Customizing Visualizations: Adjust colors, labels, and scales to improve clarity and impact.
  For example, add gridlines or change plot styles for better readability.
  • Practical: Create visualizations like correlation heatmaps or distribution plots for a dataset,
  learning to highlight key patterns.

•  Advanced tools enhance interactivity and complexity in visualizations.
  • Seaborn for Statistical Plots: Seaborn simplifies complex plots like pair plots (showing
  pairwise relationships) and violin plots (showing distributions).
  • Interactive Plots with Plotly: Plotly creates interactive visualizations (e.g., zoomable charts)
  for web-based dashboards, enhancing user engagement.
  • Practical: Build interactive visualizations, such as a dashboard showing trends in a dataset, to practice advanced visualization techniques.

 SQL(Structured Query Language) is used to manage and query relational databases, a key skill for accessing structured data.
• Database Concepts and Schema Design: Understand tables, primary keys, and relation
ships (e.g., one-to-many). Schema design ensures efficient data storage. • Queries: SELECT retrieves data, JOIN combines tables, GROUP BY aggregates data (e.g.,  total sales by region), and subqueries handle complex queries.
• Practical: Query a sample SQLite database (e.g., retail data) to extract insights, such as top-selling products or customer demographics.

Objectives
Understand statistical foundations and introduce machine learning with scikit-learn.

 Descriptive statistics summarize data, providing insights into its characteristics.
• Mean, Median, Mode, Variance, Standard Deviation: Mean is the average, median is the
middle value, and mode is the most frequent. Variance and standard deviation measure data spread.
• Probability Distributions: Understand normal, binomial, and Poisson distributions, which
model real-world phenomena like customer arrivals.
• Practical: Compute statistics (e.g., average sales, data variability) on a dataset to summarize its properties

What is Ensemble Learning

oInferential statistics draw conclusions from data samples.
• Hypothesis Testing, P-values: Test hypotheses (e.g., Is the mean sales different between
regions?) using p-values to assess significance.
• Confidence Intervals: Estimate parameter ranges (e.g., average customer spend) with con
fidence levels.
• Practical: Perform t-tests or chi-square tests on sample data to validate hypotheses

Machine learning enables systems to learn from data for predictions or decisions.
• Supervised vs. Unsupervised Learning: Supervised learning uses labeled data (e.g., pre
dicting house prices), while unsupervised learning finds patterns (e.g., clustering customers).
• Linear Regression, Logistic Regression: Linear regression predicts continuous outcomes
(e.g., prices), while logistic regression predicts categories (e.g., buy/not buy).
• Model Evaluation: Train-test split divides data to assess performance. Cross-validation
ensures robust evaluation.
• Practical: Build and evaluate a regression model using scikit-learn, understanding model  fitting and metrics like mean squared error.

• House Price Prediction: Use scikit-learns linear regression to predict house prices (e.g.,
Kaggles Boston Housing dataset). Clean data, select features (e.g., square footage, bed
rooms), train the model, and evaluate performance using metrics like R-squared.

1. Objectives
   Explore advanced machine learning algorithms and techniques for model improvement.
    Decision Trees and Random Forests
   Tree-based models are powerful for classification and regression.
   • Tree-based Models: Decision trees split data based on feature thresholds (e.g., Is age > 30?)
   to make decisions.
   • EnsembleMethods: Randomforestscombinemultipletreestoimproveaccuracy and reduce overfitting.
   • Practical: Train a random forest model on a dataset to predict outcomes, analyzing feature
   importance.

Advanced algorithms address complex data patterns.
• SVMforClassification: SupportVectorMachinesfindoptimalboundariestoseparateclasses
(e.g., spam vs. non-spam emails).
• K-means Clustering: Groups data into clusters based on similarity, useful for customer segmentation.
• Practical: Apply K-means to cluster customer data and SVM to classify data points.

Optimization improves model performance and generalizability.
• Hyperparameter Tuning with GridSearchCV: Test combinations of parameters (e.g., tree
depth) to find the best model configuration.
• Feature Selection and Engineering: Select relevant features and create new ones (e.g.,
combining age and income) to enhance model accuracy.
• Practical: Optimize a models performance using GridSearchCV and feature engineering
techniques

Car Price Prediction: Build a machine learning model (e.g., random forest or gradient
boosting) using a dataset (e.g., Kaggles used car dataset). Preprocess data, engineer features
(e.g., car age), and optimize the model for accurate price predictions

 Objectives
Master deeplearning concepts and convolutional neural networks (CNNs) with TensorFlow/Keras.

Deep learning uses neural networks to model complex patterns.
• Neural Networks and Activation Functions: Neural networks consist of layers of nodes
that process data. Activation functions (e.g., ReLU, sigmoid) introduce non-linearity for
complex modeling.
• Backpropagation and Optimization: Backpropagation adjusts weights to minimize errors.
Optimizers (e.g., Adam) update weights efficiently.
• Practical: Build a simple neural network for a regression or classification task using Tensor
Flow/Keras

 Advanced neural network techniques improve model performance.
• Building and Training Deep Neural Networks: Stack multiple layers to model complex
data. Train models using epochs and batch sizes.
• Handling Overfitting: Use dropout (randomly disabling nodes) and regularization (penaliz
ing large weights) to prevent overfitting.
• Practical: Train a deep neural network on a tabular dataset, applying techniques to reduce
overfitting

 CNNs are specialized for image data, critical for computer vision.
• CNNArchitecture: Convolution layers extract features (e.g., edges), pooling layers reduce
dimensions, and fully connected layers make predictions.
• Image Preprocessing: Normalize pixel values and augment data (e.g., rotate images) to
improve model robustness.
• Practical: Build a CNN for image classification, experimenting with different architectures

Cat-Dog Classification: Use a CNN to classify images of cats and dogs (e.g., Kaggles Cats
vs Dogs dataset). Preprocess images, train the model, and evaluate accuracy, exploring tech
niques like data augmentation

 Objectives
Learn NLP techniques and build a chatbot as a capstone project.
 NLPFundamentals
NLPenables computers to process and analyze text data.
• Text Preprocessing: Tokenization splits text into words, stemming/lemmatization reduces  words to their root forms, and stop-word removal eliminates common words (e.g., the).

• BagofWords,TF-IDF: Bagofwordsrepresents text as word counts, while TF-IDF weights
words by importance, useful for text classification.
• Practical: Process and analyze text data (e.g., reviews) to create feature representations

 Sentiment analysis classifies text as positive, negative, or neutral.
• Word Embeddings: Word2Vec and GloVe represent words as vectors capturing semantic
relationships (e.g., king is close to queen).
• Sentiment Classification with LSTM: Long Short-Term Memory networks model sequen
tial data, ideal for text analysis.
• Practical: Build a sentiment analysis model using LSTM to classify text data

Chatbots simulate human conversation using NLP techniques.
• Sequence-to-Sequence Models: These models map input text to output responses, suitable
for dialogue systems.
• Hugging Face Transformers: Leverage pre-trained models (e.g., BERT) for advanced NLP
tasks, fine-tuning for specific applications.
• Practical: Build a simple rule-based or neural chatbot, experimenting with dialogue genera
tion.

Sentiment Analysis: Develop a model to classify movie reviews as positive or negative (e.g.,
IMDb dataset) using LSTM or transformers, evaluating accuracy and F1-score.
• ChatbotBuilding: Create a chatbot (e.g., a customer service bot) using Hugging Face Trans
formers, integrating intents and responses for practical use.