1. Students and Recent Graduates:

• Computer Science: Students or graduates with a background in computer science, engineering, or related fields who want to specialize in data science.
• Mathematics/Statistics: Students or graduates with a background in mathematics or statistics interested in applying their skills to real-world data problems.
• Natural Sciences: Students or graduates from disciplines like physics, biology, or chemistry looking to transition into data-driven research or analysis roles.
• Business/Economics: Students or graduates interested in business analytics, market research, or economics who want to incorporate data science techniques into their work.

2. Professionals Seeking Career Transition or Advancement:

• IT Professionals: Professionals from IT backgrounds (software development, database management) seeking to pivot into data science roles.
• Analysts: Business analysts or financial analysts looking to enhance their analytical skills with data science techniques.
• Researchers/Academics: Professionals from research or academic backgrounds interested in using data science for research analysis and publication.
• Career Changers: Individuals from non-technical fields looking to transition into data science through structured learning and practical training.

3. Requirements and Prerequisites:

• Technical Skills: While not always required, familiarity with programming languages such as Python or R, and basic knowledge of statistics and mathematics are advantageous.
• Educational Background: Many courses accept applicants with diverse educational backgrounds. Some may require a bachelor’s degree in a relevant field, while others may accept applicants based on relevant experience or skills.

1. Data Analyst:

•  Responsibilities include data cleaning, analysis, and visualization to derive insights that inform business decisions.
•  Industries: Finance, marketing, healthcare, e-commerce, etc.

2. Data Scientist:

•  Involves advanced analytics, machine learning model development, and predictive modelling to solve complex problems.
• Industries: Technology, healthcare, finance, retail, telecommunications, etc.

3. Machine Learning Engineer:

•  Focuses on designing and implementing machine learning systems, optimizing algorithms, and deploying models into production.
• Industries: Tech companies, finance, healthcare, autonomous vehicles, etc.

4. AI Research Scientist:

•  Conducts research to advance artificial intelligence techniques, including deep learning, natural language processing, and computer vision.
•  Industries: Research institutions, tech giants, healthcare, autonomous systems, etc.

5. Business Intelligence (BI) Analyst:

•  Uses data analysis and visualization tools to provide insights into business performance and support strategic decision-making.
•  Industries: Business consulting, finance, marketing, retail, etc.

6. Data Engineer:

•  Builds and manages data pipelines, warehouses, and infrastructure to ensure efficient data collection, storage, and retrieval.
• Industries: Tech companies, finance, healthcare, e-commerce, etc.

7. Quantitative Analyst (Quant):

•  Applies mathematical and statistical models to financial and risk management problems, often in finance and investment firms.
•  Industries: Banking, investment management, hedge funds, insurance, etc.

8. Data Architect:

•  Designs and oversees the organization's data strategy, including data modelling, database design, and integration with other systems.
• Industries: Enterprise IT, healthcare, finance, government, etc.

9. Data Consultant:

•  Provides expertise in data science and analytics to help businesses optimize processes, improve decision-making, and drive growth.
•  Industries: Consulting firms, technology services, healthcare, finance, etc.

1. Data-Driven Decision Making: Data science enables organizations to make decisions based on data analysis rather than intuition or guesswork. This leads to more accurate and reliable decisions.

2. Improved Efficiency: By automating repetitive tasks, data science can significantly improve operational efficiency. Algorithms can process large datasets quickly and perform complex calculations efficiently.

3. Innovation: Data science fosters innovation by uncovering new patterns, correlations, and insights from data that may not be apparent through traditional analysis methods.

4. Competitive Advantage: Organizations that effectively utilize data science are often able to gain a competitive edge. They can optimize processes, predict trends, and respond swiftly to changes in the market.

5. Better Targeting and Personalization: Through techniques like predictive analytics and machine learning, businesses can better understand their customers' preferences and behavior, allowing for targeted marketing campaigns and personalized customer experiences.

6. Cost Savings: Data science can identify inefficiencies and areas where costs can be reduced. For example, predictive maintenance in manufacturing can prevent equipment failures and reduce downtime.

7. Risk Management: Data science plays a crucial role in risk assessment and management across various industries, such as finance and insurance. It helps in identifying potential risks and implementing strategies to mitigate them.

8. New Revenue Streams: By analyzing data, businesses can discover opportunities for new products or services that meet customer needs or fill gaps in the market.

9. Improved Customer Insights: Data science enables a deeper understanding of customer preferences, allowing businesses to tailor their offerings and improve customer satisfaction and retention.

10. Scientific Advancements: In fields like healthcare and research, data science facilitates advancements by analyzing large datasets to uncover patterns in diseases, genetics, climate change, and more.

Data science finds applications in various industries and domains, including:

• Business and Finance: Predictive analytics for customer behaviour, fraud detection, risk management.
• Healthcare: Disease prediction, personalized medicine, medical image analysis.
• Marketing and E-commerce: Customer segmentation, recommendation systems, market basket analysis.
• Manufacturing and Operations: Predictive maintenance, supply chain optimization, quality control.
• Social Sciences: Sentiment analysis, opinion mining, social network analysis.
• Technology: Product analytics, user behaviour analysis, AI-driven applications.
• Telecommunications: Network optimization, customer churn prediction.
• Government and Public Sector: Policy analysis, resource allocation, predictive policing.

1. Data Collection and Acquisition:

Data science begins with identifying relevant data sources and collecting raw data. This can include structured data from databases, unstructured data from social media or text documents, or semi-structured data from APIs.

2. Data Cleaning and Preprocessing:

Raw data often requires cleaning and preprocessing to remove noise, handle missing values, standardize formats, and transform data into a usable format for analysis. This step ensures data quality and reliability.

3. Exploratory Data Analysis (EDA):

EDA involves analyzing and visualizing data to understand its characteristics, identify patterns, and uncover insights that may guide further analysis. Techniques such as statistical summaries, data visualization, and dimensionality reduction are used.

4. Feature Engineering:

Feature engineering involves selecting, transforming, and creating features (variables) from the raw data that are relevant for modelling. It aims to improve the performance of machine learning algorithms by providing meaningful input data.

5. Machine Learning and Statistical Modelling:

Machine learning algorithms are applied to build predictive models and make data-driven decisions. This includes supervised learning (e.g., classification, regression), unsupervised learning (e.g., clustering, dimensionality reduction), and sometimes reinforcement learning.

6. Evaluation and Validation:

Models are evaluated using various metrics to assess their performance and generalizability. Cross-validation techniques and validation sets are used to ensure models perform well on unseen data and avoid overfitting.

7. Deployment and Integration:

Successful models are deployed into production environments where they can make predictions or automate decisions based on new data. Integration with existing systems and continuous monitoring are essential for maintaining model performance.

8. Ethics and Interpretability:

Data science also involves ethical considerations regarding data privacy, fairness, and transparency. Interpretable models and explanations of predictions help ensure decisions are understandable and trustworthy.

1.Statistics: Fundamental principles of statistics are crucial for data analysis, including descriptive statistics, probability theory, hypothesis testing, and regression analysis.

2.Machine Learning: Techniques and algorithms that enable machines to learn from data and make predictions or decisions, including supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, dimensionality reduction), and reinforcement learning.

3.Data Mining: The process of discovering patterns and relationships in large datasets using techniques from machine learning, statistics, and database systems.

4.Data Cleaning and Preprocessing: Techniques for cleaning, transforming, and preparing raw data for analysis, including handling missing data, outliers, and normalization.

5.Big Data Technologies: Tools and frameworks for storing, processing, and analyzing large volumes of data, including distributed computing platforms like Hadoop and Spark.

6.Data Visualization: Techniques and tools for creating visual representations of data to facilitate exploration, analysis, and communication of insights.

7.Data Wrangling: The process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes.

8.Feature Engineering: The process of selecting, extracting, and transforming features (variables) in data to improve the performance of machine learning models.

9.Deep Learning: A subfield of machine learning focused on algorithms inspired by the structure and function of the brain called artificial neural networks. It has achieved state-of-the-art results in tasks such as image and speech recognition.

10.Natural Language Processing (NLP): Techniques for analyzing and interpreting human language, including tasks such as sentiment analysis, language translation, and text generation.

11.Database Systems: Knowledge of database management systems (SQL, NoSQL) and their role in data storage, retrieval, and management.

12.Ethics and Privacy: Considerations around ethical issues related to data collection, usage, and privacy, including regulations such as GDPR and ethical guidelines for AI.

13.Domain Knowledge: Understanding of the specific domain or industry where data science techniques are applied, such as healthcare, finance, marketing, or social sciences.

Online Weekend Sessions: 27 to 30 | Duration: 5 to 60 Hours

Foundations of Data Science

1. Introduction to Data Science

•  Overview of data science, its importance, and applications.
•  Role of data scientists and data engineers.

2. Mathematics and Statistics for Data Science

• Descriptive and inferential statistics.
• Probability theory.
• Linear algebra and calculus.
• Optimization techniques.

3. Programming for Data Science

• Programming languages commonly used in data science (e.g., Python, R).
• Data manipulation and cleaning.
• Data visualization techniques. Data Collection and Storage

4. Data Collection and Cleaning

• Techniques for collecting data from various sources (e.g., APIs, databases).
• Cleaning and preprocessing raw data.
• Dealing with missing data and outliers.

5. Data Storage and Management

• Relational databases (SQL) and non-relational databases (NoSQL).
• Querying databases for data retrieval and manipulation. Data Analysis and Machine Learning

6. Exploratory Data Analysis (EDA)

• Techniques for exploring and summarizing data.
• Data visualization tools and libraries.

7. Machine Learning Fundamentals

• Supervised learning (regression, classification).
• Unsupervised learning (clustering, dimensionality reduction).
• Evaluation metrics for machine learning models.

8. Advanced Machine Learning

• Ensemble methods (e.g., random forests, gradient boosting).
• Deep learning fundamentals (neural networks, convolutional neural networks, recurrent neural networks).
• Natural Language Processing (NLP) and text mining. Big Data and Distributed Computing

9. Big Data Technologies

• Introduction to big data concepts.
• Distributed computing frameworks (e.g., Hadoop, Spark).

10. Data Engineering

• Data pipelines and workflow management.
• Real-time data processing. Applied Data Science

11. Case Studies and Applications

• Practical applications of data science in various domains (e.g., healthcare, finance, marketing).
• Real-world projects and challenges.

12. Ethics and Privacy

• Ethical considerations in data science (e.g., bias, privacy, transparency).
• Regulatory frameworks (e.g., GDPR, HIPAA). Capstone Project

13. Capstone Project

• A comprehensive data science project that integrates concepts learned throughout the course.
• Project design, implementation, and presentation. Additional Topics

14. Special Topics in Data Science

• Depending on the program, additional topics may include time series analysis, recommender systems, spatial data analysis, etc. Professional Development

15. Career Development in Data Science

• Job roles and responsibilities in data science.
• Building a data science portfolio.
• Interview preparation and networking.