Comprehensive Data Science Training Course Content

Module 1: Introduction to Data Science (3 Hours)

What is Data Science?
Roles and responsibilities of a data scientist
Data science lifecycle and methodology
Overview of tools and technologies
Setting up your data science environment (Anaconda, Jupyter, IDEs)

Module 2: Python for Data Science (5 Hours)

Python basics review (variables, data types, control flow)
Libraries for data science: NumPy, Pandas, Matplotlib, Seaborn
Data manipulation with Pandas
Data visualization techniques and best practices
Working with datasets and data frames

Module 3: Statistics and Probability for Data Science (5 Hours)

Descriptive statistics (mean, median, mode, variance, standard deviation)
Probability concepts and distributions (normal, binomial, Poisson)
Inferential statistics (hypothesis testing, confidence intervals)
Correlation and covariance
Sampling techniques and bias

Module 4: Data Wrangling and Exploration (4 Hours)

Data cleaning and preprocessing
Handling missing data and outliers
Feature engineering and selection
Exploratory data analysis (EDA)
Data transformation and scaling

Module 5: Machine Learning Fundamentals (6 Hours)

Introduction to supervised and unsupervised learning
Regression techniques (linear, logistic)
Classification algorithms (k-NN, Decision Trees, Random Forest)
Clustering algorithms (k-means, hierarchical)
Model evaluation metrics (accuracy, precision, recall, F1-score)

Module 6: Advanced Machine Learning (6 Hours)

Support Vector Machines (SVM)
Ensemble methods (Boosting, Bagging, XGBoost)
Dimensionality reduction (PCA, t-SNE)
Model tuning and hyperparameter optimization
Handling imbalanced data

Module 7: Deep Learning Basics (5 Hours)

Introduction to neural networks
Frameworks overview (TensorFlow, Keras, PyTorch)
Building simple neural networks
Activation functions, loss functions
Training and evaluation of deep learning models

Module 8: Natural Language Processing (NLP) (4 Hours)

Text preprocessing (tokenization, stemming, lemmatization)
Bag of Words and TF-IDF
Sentiment analysis
Introduction to word embeddings (Word2Vec, GloVe)
Basic NLP tasks and libraries (NLTK, spaCy)

Module 9: Data Visualization and Reporting (3 Hours)

Advanced visualization with Matplotlib and Seaborn
Interactive dashboards with Plotly and Dash
Storytelling with data
Reporting best practices

Module 10: Big Data and Cloud for Data Science (4 Hours)

Introduction to Big Data concepts
Working with Hadoop and Spark
Cloud platforms overview (AWS, Azure, GCP)
Using cloud services for data science workloads

Module 11: Model Deployment and Monitoring (3 Hours)

Model serialization and saving (pickle, joblib)
Creating REST APIs for models (Flask, FastAPI)
Introduction to MLOps and model monitoring
Automating workflows with pipelines

Module 12: Capstone Project (7 Hours)

Project planning and data collection
Exploratory data analysis and feature engineering
Model building and evaluation
Visualization and storytelling
Presentation and documentation

Capstone

Sales Performance Dashboard

Learn to create interactive dashboards in Excel to visualize key sales metrics, analyze performance trends, and support data-driven decision-making using functions and pivot tables.

Capstone

Customer Data Analysis (Python & Pandas)

Clean, process, and visualize customer datasets using Python and Pandas to identify data quality issues, uncover behavioral patterns, and extract actionable insights.

Capstone

Unlocking Customer Insights: A Statistical Investigation

Apply descriptive statistics, data visualization, and hypothesis testing to investigate business questions and reveal insights hidden in customer or operational data.

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New Age Curriculum

40+ Hours of learning of new tools, models and projects.

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Get access to recorded videos and assessments.

Who Should Do The Comprehensive Data Science Course?

Aspiring Analysts
Individuals looking to start a career in data analysis or analytics-related fields.
Career Changers
Professionals from non-technical backgrounds eager to transition into the high-demand field of data analytics.
IT & Engineering Graduates
Students or professionals with technical degrees who want to specialize in data analysis.
Business Professionals
Managers or executives aiming to make data-driven decisions and enhance business outcomes.
Freelancers & Consultants
Professionals who want to offer data analysis as a service to clients.
Upskilling Professionals
Those in adjacent fields like marketing, finance, or operations who want to add data analytics to their skill set.

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Comprehensive Data Science Training Course Content

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