Finest Data Science Course in Pune : Data Science Classes in Pune

Box Plot: Learn to identify outliers and visualize data spread.

Random Variable: Introduction to discrete and continuous variables.

Probability: Basics of probability, rules, and real-world applications.

Probability Distribution: Binomial, Poisson, and normal distributions explained.

Normal Distribution: Characteristics and importance in data analysis.

Standard Normal Distribution (SND): Z-scores and standard deviations.

Expected Value: Calculate the mean of probability distributions.

Sampling Funnel: Steps and stages of data sampling.

Sampling Variation: Effects of sample size and variability.

Central Limit Theorem: Importance in inferential statistics.

1. Null and alternative hypotheses.
2. Hypothesis Testing Techniques:
   • 2 proportion test
   • 2 sample t-test
3. Anova and Chi-Square: Understand variance analysis and categorical data testing.

1. Principles of Regression: Basics of regression analysis.
2. Introduction to Simple Linear Regression: Regression line and equation.
3. Multiple Linear Regression: Incorporating multiple predictors.
4. Logistic Regression: Binary outcome predictions.

1. Imputation Techniques: Mean, median, and advanced methods.
2. Data Analysis and Visualization: Insights through graphs and plots.
3. Scatter Diagram: Analyze relationships between variables.
4. Correlation Analysis: Pearson and Spearman coefficients.
5. Transformations: Log, square root, and box-cox transformations.
6. Encoding Methods:
   • One-Hot Encoding (OHE)
   • Label Encoding
7. Outlier Detection:
   • Isolation Forest
   • Predictive Power Score (PPS)

1. Clustering Introduction: Fundamentals of grouping data.
2. K-Means Clustering: Partitioning methods for clusters.
3. Association Rules: Identify relationships between variables.
4. Content-based and collaborative filtering.
5. Basics of deploying ML models in Python.

1. Basics of predictive modeling in ML DL.
2. Explainable models for decision-making.
3. Instance-based learning.
4. Linear and non-linear classifiers.
5. Feature Engineering:
   • Tree-based methods
   • Recursive Feature Elimination (RFE)
   • PCA
6. Model Validation Methods:
   • Train-test split
   • Cross-validation
   • Shuffle CV
7. Regularization:
   • Lasso Regression
   • Ridge Regression

1. Artificial Neural Networks 
2. Optimization Algorithms
3. Back Propagation: Fundamentals of weight updates.

1. Bagging and Random Forest: Ensemble learning techniques.
2. Boosting:
   • XGBoost
   • LightGBM (LGBM)

1. Introduction to Text Mining: Basics of textual data processing.
2. Vector Space Model (VSM): Representing text data numerically.
3. Introduction to Word Embeddings: Basics of word vectors.
4. Word Clouds: Visualization techniques.
5. Document Similarity: Using cosine similarity.
6. Named Entity Recognition (NER): Extracting entities from text.
7. Text Classification: Using Naive Bayes.
8. Emotion Mining: Sentiment analysis techniques.

1. Introduction to Time Series: Basics and components.
2. Level, Trend, and Seasonality: Decomposing time series data.
3. Lag Plot: Identifying relationships over time.
4. Autocorrelation Function (ACF): Assessing dependencies.
5. Principles of Visualization: Plotting techniques for time series.
6. Forecasting Errors: Metrics for accuracy.
7. Model-Based Approaches:
   • ARIMA

1. Programming Cycle of Python
2. Python IDEs: Jupyter Notebook and others.

1. Introduction to Variables
2. Data Types: Overview of Python data types.

1. GitHub
2. HackerRank
3. CodeWars
4. Sanfoundry

1. Operators: Arithmetic and comparison.
2. Decision Making with Loops:
   • While loop
   • For loop
   • Nested loops
3. String Operations:
   • Escape characters
   • String formatting

1. Lists: Indexing, slicing, and matrices.
2. Tuples: Immutable sequences.
3. Dictionaries: Key-value pairs and operations.

1. Functions: Defining, calling, and recursion.
2. Modules: Importing and managing modules.

1. Files: Opening, reading, and writing.
2. Directories: Managing folders.

1. Error Types
2. Try-Except Blocks

1. Classes and Objects
2. Inheritance
3. Method Overloading

1. Pattern Matching
2. Modifiers

1. SQLite and MySQL
2. Database Connectivity

1. Databases
2. Introduction to RDBMS
3. Different Types of RDBMS
4. MySQL Workbench

1. Data Definition Language
2. Data Manipulation Language (DML)
3. Data Query Language (DQL):
   Explore how the SELECT command is used to retrieve data from databases, along with various filtering techniques.
4. Transactional Control Language
5. Discover the role of GRANT and REVOKE commands in controlling user permissions and database security.

1. SELECT and LIMIT:
   Learn to query data using SELECT and restrict results with the LIMIT clause for better performance.
2. DISTINCT and WHERE:
   Filter unique records using DISTINCT and apply conditions with the WHERE clause for precise results.
3. AND, OR, and IN Operators:
   Combine multiple conditions using logical operators like AND, OR, and IN to refine queries.
4. NOT IN and BETWEEN:
   Exclude specific values using NOT IN and query data within a range using BETWEEN.
5. EXIST, ISNULL, and IS NOT NULL:
   Use EXIST to check record existence and handle null values with ISNULL and IS NOT NULL.

Wildcards:
Master pattern matching in SQL queries using wildcards like % and _.

1. Aggregate Functions
2. String Functions
3. Date & Time Functions

1. NOT NULL and UNIQUE:
   Ensure data integrity by enforcing NOT NULL constraints and maintaining unique values with UNIQUE.
2. CHECK and DEFAULT:
   Apply conditions to data with CHECK and assign default values using DEFAULT.
3. ENUM:
   Limit values in a column to predefined options using ENUM.
4. Primary Key and Foreign Key: Understand how primary keys uniquely identify rows, while foreign keys establish relationships between tables.

1. Inner Join
2. Left and Right Joins:
   Learn how LEFT JOIN and RIGHT JOIN include unmatched rows from one table.
3. Cross and Full Outer Joins
4. Self Joins: Understand how a table can join itself for advanced relational queries.

1. Indexes:
   Learn how indexes improve query performance by optimizing data retrieval.
2. Views:
   Create virtual tables with VIEW for simplified data representation and access control.
3. Sub-queries:
   Master nested queries to perform complex data retrieval in a modular manner.
4. Window Functions
5. Stored Procedures and Exception Handling:
   Automate repetitive tasks with stored procedures and handle errors gracefully during execution.
6. Loops and Cursors:
   Implement iterative processes and manage result sets dynamically using loops and cursors.
7. Triggers

1. NNs
2. Importance of Deep Learning:
   Discussing the strengths of deep learning in handling vast data, non-linear relationships, and limitations such as overfitting and computational requirements.
3. Neural Network Types
4. Neural Network Representation
5. Activation Functions
6. Loss Functions:
   Understanding the role of loss functions in measuring model accuracy and guiding optimization.
7. Gradient Descent: Overview of gradient descent algorithms and their role in minimizing loss functions.

1. Train, Test & Validation Sets:
   Explanation of how datasets are split into training, testing, and validation for building and evaluating models.
2. Vanishing & Exploding Gradients:
   Challenges with gradient propagation in deep networks and strategies to mitigate them.
3. Dropout Regularization
4. Overview of algorithms like Adam, SGD, and RMSProp for efficient model training.
5. Learning Rate Tuning:
   The impact of learning rates on convergence and methods for fine-tuning.
6. Softmax Function

1. Introduction to CNNs:
   Understanding how CNNs are designed for image and spatial data processing.
2. Deep Convolutional Models:
   Overview of advanced architectures like VGGNet, ResNet, and their use cases.
3. Detection Algorithms:
   Exploring object detection algorithms such as YOLO and SSD.
4. CNN for Face Recognition: Application of CNN in facial recognition systems and real-world implementations.

1. Introduction to RNNs
2. Challenges in RNNs:
   Issues like vanishing gradients in RNNs and techniques to address them.
3. LSTM Networks
4. How Bidirectional LSTMs improve model performance in sequential tasks.