Data Science:

Lesson 1 – Introduction to Data Science (Duration-2 Hours)

• Definition and scope of data science
• Exploration of the importance and diverse applications of data science across various industries
• Examination of the historical background and evolutionary trajectory of data science

Lesson 2 – Statistics (Duration-4 Hours)

• Overview of basic statistical concepts
• Understanding to probability distributions
• Differentiation between descriptive & inferential statistics
• Introduction to hypothesis testing methodologies and significance levels

Lesson 3 - Information Visualisation (Duration-3 Hours)

• Understanding Data Visualization, its tools & libraries such as Matplotlib, Seaborn, etc.
• Techniques for data visualization, distributions, relationships, and patterns
• Interpretation of visualizations to extract valuable insights from the data

Lesson 4 - Data Mining, Structures, and Manipulation (Duration-5 Hours)

• Understanding the basics of data mining and its importance in extracting valuable insights from massive datasets.
• Exploring the process of data mining and its applications in various industries.
• Discussing the challenges & ethical guidelines in data mining.
• Comprehension of the attributes and utility of lists and tuples
• Execution of operations to manipulate lists and tuples
• Utilization of list comprehensions for streamlined data manipulation
• Introduction to dictionaries and sets as primary data structures

Lesson 5 - Algorithms used in Machine Learning (Duration-4 Hours)

• Introduction to machine learning & its algorithms
• Implementing supervised and unsupervised learning techniques

Lesson 6 - Data Acquisition & Data Science Life Cycle (Duration-4 Hours)

• Examination of data sources and categorization into structured and unstructured data
• Survey of diverse data collection methods
• Exploration of various data storage formats such as databases, spreadsheets, JSON, and CSV
• Deconstruction of the data science process into key stages
• Identification of the pivotal role of a data scientist at each stage of the lifecycle
• Illustration of the data science lifecycle

Lesson 7 – Data Pre-processing (Duration-4 Hours)

• Illustration of data cleaning & pre-processing techniques
• Strategies for handling missing values and outliers in datasets
• Introduction to feature engineering methods for creating new data attributes

Lesson 8 - Experimentation, Evaluation and Project Deployment Tools (Duration-6 Hours)

• Overview of popular data science tools and platforms including Jupyter Notebook, RStudio, and Anaconda
• Introduction to version control systems such as Git for collaboration and project management
• Guidance on setting up a data science environment, including installation and configuration of necessary tools and libraries

Lesson 9 - Predictive Analytics and Segmentation using Clustering (Duration-4 Hours)

• Introduction to clustering algorithms for grouping similar data points together.
• Exploring unsupervised learning algorithms such as k-means clustering and hierarchical clustering.
• Discussing methods for evaluating the quality of clustering results.
• Exploring machine learning algorithms for predictive analysis.
• Applying advanced techniques to analyze real-world datasets.

Lesson 10 - Working on Real World Projects (Duration-4 Hours)

• Engaging in hands-on projects involving real-world data visualization tasks.
• Applying acquired skills & techniques to tackle data-centric challenges.
• Presenting project outcomes and insights to peers for constructive feedback and discussion.

Lesson 11 - Big Data Fundamentals and Hadoop Integration with R (Duration-5 Hours)

• Getting familiar with big data and why it's important in today's world.
• Learning about the main features of big data: how much there is, how fast it comes, how varied it is, and how reliable it needs to be.
• Exploring the challenges and opportunities that come with working with big data.
• Discovering the different parts of the Hadoop ecosystem and what they do.
• Understanding how Hadoop's file system helps store lots of data across many machines.

Data Analytics:

Lesson 1 - Introduction to Data Analytics (Duration-1 Hour)

• Recognizing the significance and practical applications of data analysis.
• Introducing fundamental concepts and principles essential for proficient data analysis.
• Surveying prevalent software and tools employed in the field of data analysis.

Lesson 2 - Introduction to R (Duration-1 Hour)

• Understanding R language syntax and structure
• Getting started with RStudio IDE
• Fundamentals of variables, data types, and operations in R
• Control structures: loops and conditions

Lesson 3 - R Basics (Duration-5 Hours)

• Exploring vectors, matrices, arrays, and data frames
• Manipulating and accessing different data structures
• Applying functions to perform operations on data structures

Lesson 4 - R Packages (Duration-2 Hours)

• Utilizing dplyr for data manipulation tasks
• Tidying data with tidyr: reshaping and transforming
• Filtering, selecting, arranging, and summarizing data efficiently

Lesson 5 - Importing Data (Duration-2 Hours)

• Importing and exporting data from various sources
• Reading and writing data from/to databases
• Managing different file formats in R

Lesson 6 - Manipulating Data (Duration-3 Hours)

• Advanced data manipulation techniques
• Working with time-series data and forecasting
• Introduction to NLP and text mining
• Integration of R with other tools for advanced analytics

Lesson 7 - Error Metrics (Duration-3 Hours)

• Implementing data cleaning methodologies to fix errors.
• Strategizing category wise data handling through encoding and transformation techniques.

Lesson 8 - Machine Learning (Duration-5 Hours)

• Understanding artificial intelligence.
• Exploring the history and evolution of artificial intelligence.
• Surveying the diverse applications of artificial intelligence across various industries.
• Discussing the ethical implications and societal impact of artificial intelligence.
• Introducing the fundamental principles of machine learning.
• Understanding the difference between supervised, unsupervised, and reinforcement learning.
• Exploring key machine learning algorithms such as linear regression, decision trees, and k-nearest neighbors.

Lesson 9 - Supervised Learning (Duration-6 Hours)

• Understanding supervised learning concepts and objectives.
• Implementing regression algorithms for predicting continuous outcomes.
• Exploring classification algorithms for predicting categorical outcomes.

Lesson 10 - Unsupervised Learning (Duration-4 Hours)

• Understanding unsupervised learning methodologies.
• Exploring clustering algorithms like K-means clustering and hierarchical clustering.
• Dimensionality reduction techniques.
• Applying unsupervised learning for data exploration & pattern recognition.

Lesson 11 – Natural Language Processing (Duration-7 Hours)

• Understanding and working with human language in computers.
• Discovering how NLP has evolved over time and where it's used today.
• Exploring how NLP helps in different fields like healthcare, finance, and customer service.
• Getting started with preparing text for analysis.
• Learning simple methods to break text into smaller parts, find root words, and fix common mistakes.
• Understanding why we remove certain words and punctuation from text and how we make spelling consistent.
• Diving into more advanced topics in NLP, like answering questions, having conversations, and understanding language better.
• Exploring new ideas and technologies in NLP, like big language models that learn from lots of text.
• Trying out what we've learned in practical projects and seeing how NLP can help solve real-world problems.

Lesson 12 – Deep Learning (Duration-6 Hours)

• Basic understanding of deep learning and neural networks.
• Investigating the historical context and evolution of deep learning.
• Exploring the diverse applications and significance of deep learning across various domains.
• Familiarizing with artificial neural networks (ANNs) and their architecture.
• Understanding neurons, layers, and activation functions.
• Delving into the mechanisms of feedforward and backpropagation algorithms employed in training ANNs.
• Surveying prominent deep learning frameworks such as TensorFlow and PyTorch.
• Hands-on engagement with constructing and training neural networks using these frameworks.
• Merits and limitations of different deep learning frameworks.