DATA SCIENCE

🔹 1. Basics of Data Science

• Data science: what is it?
• Machine learning, data analytics, and data science
• Applications in the real world
• A data science project's lifecycle

🔹2. Data Science and Mathematics

• Algebraic Linear Vectors and Matrix
• Transformations and operations on matrices

📊 Statistics and Probability

• Mean, median, mode, and variance are examples of descriptive statistics.
• Distributions of probabilities (normal, Poisson, and binomial)
• The Bayes theorem
• P-value analysis and hypothesis testing

📈 Calculus gradients and derivatives

• Fundamentals of optimization (gradient descent)

🔹3. Data Science Programming Python (Main Language)

• Loops, functions, data types, and OOP
• Libraries: Seaborn, Matplotlib, Pandas, and NumPy

R (Optional): Fundamental statistical modeling and syntax

• Representation using ggplot2

🔹4. Handling and Manipulation of Data

• Reading and writing data in Excel, JSON, and CSV
• Data cleaning and wrangling
• Managing outliers and missing data
• combining, reshaping, and merging datasets
• Data handling for dates and times

🔹5. Visualization of Data

• Box plots, scatter plots, histograms, lines, and bars
• Heatmaps and matrices of correlation
• Interactive visualizations (Basics of Tableau/Power BI, Dash, and Plotly)

🔹6. Exploratory Data Analysis (EDA)

• Understanding and choosing features
• Analysis that is univariate, bivariate, or multivariate
• Covariance and correlation
• Visual patterns and summary statistics

🔹 7. SQL and databases

• Schemas and relational databases
• SQL queries: subqueries, SELECT, JOIN, and GROUP BY
• Fundamentals of NoSQL (MongoDB)

🔹8. Supervised and Unsupervised Machine Learning 📚 Supervised Learning

• Logistic regression and linear regression
• Random forests and decision trees
• SVMs, or support vector machines
• k-NN, or k-Nearest Neighbors
• Clustering for Unsupervised Learning (K-means, Hierarchical)
• Reduction of dimensionality (PCA, t-SNE)

🔹 9. Model Assessment and Improvement

• Cross-validation with train/test separation
• F1-score, recall, accuracy, and confusion matrix
• ROC-AUC charts
• Adjusting hyperparameters (Random Search, Grid Search)

🔹10. Neural networks and deep learning (intermediate/advanced)

• Feedforward and perceptron networks
• Functions of activation (ReLU, Sigmoid, Tanh)
• Optimization (SGD, Adam) with backpropagation
• RNNs and CNNs (basic summary)
• Frameworks: PyTorch, TensorFlow, and Keras

-🔹11. Preprocessing and cleaning of texts using Natural Language Processing (NLP)

• Words Bag, TF-IDF
• Word embeddings, such as GloVe and Word2Vec
• Analysis of sentiment and topic modeling

🔹12. Time Series Evaluation

• Trends and Stationarity
• Seasonality, and autocorrelation
• SARIMA and ARIMA models
• Techniques for forecasting

🔹13. Hadoop and Spark:

• Fundamentals of Big Data and Cloud Computing (intro only)
• Cloud computing platforms: S3, EC2, BigQuery, and AWS, GCP, and Azure
• An overview of distributed computing

🔹 14. Defining the Capstone Project Problem

• Data gathering and evaluation
• Building and assessing models
• Report, presentation, or dashboard

Experience in the Industry Gain knowledge from experts with practical Data Science project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as hcl, tcs , accenture  and cognizant  employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques and best practices.