POST GRADUATION IN DATA SCIENCE

This serves as an introductory module, providing insights into the current market landscape and the key elements for success in the data analytics field.

Students grasp how analytics affects businesses around the world, altering their understanding of customer trends and patterns and impacting overall revenue. Given that 95% of businesses consider managing unstructured data to be a serious concern, this session emphasizes the importance of individuals becoming skilled data professionals capable of handling and extracting insights from data.

Other than subjective knowledge the module evaluates participants' skills and aspirations toward becoming successful data scientists.

• Students gain skills such as explaining complicated ideas for beginners, growing business curiosity, recognizing market trends, and delivering exact research and answers in addition to basic topic knowledge.

• The ability to think about the larger organizational architecture rather than just specific tasks.

Participants learn how to use data science techniques in a variety of domains. Covering a range of data science and analytics tools and identifying suitable organizations for application, this module provides an overview of the entire curriculum, serving as a preparatory segment.

This section of our Data Science Course digs into statistics basics, explaining how it uncovers patterns in numerical data. It is an invaluable resource for applicants who have no prior statistical knowledge.

Concurrently, there is a refresher course for non-programmers on the fundamentals of programming. This segment dispels the misconception that computer science solely revolves around coding. Graphics, operating systems, logical functions, and theoretical computer science, computer architecture, and algorithm creation are all part of it.

Frequently, aspiring data scientists question the significance of mathematics in their career trajectory, and the answer is affirmative. While basic mathematical skills are sufficient for functional positions, growing in the sector and obtaining domain knowledge needs a more in-depth understanding of mathematics.

This talk will discuss how data scientists may research consumer behavior, which is important for firms searching for insights into user intent for targeted advertising. In today's business environment, data analytics is critical in recruiting and maintaining new clients through personalized methods.

Key Learning Objectives:

• Understanding the components of data analytics.

• Investigating qualitative and quantitative data analysis methods

The module guides participants to maximize the potential of EXCEL for data storage, analysis, and visualization. It also teaches non-technical audiences how to properly communicate data visually. It is necessary for a data scientist to not only uncover patterns and predict insights, but also to convey findings using charts, graphs, heatmaps, and so on.

Further Learnings:

• Different types of visual representations.

• Techniques for visualizing data.

  • Data visualization tools: Tableau, Google Data Studio, Power BI.

  • The benefits and drawbacks of data visualization techniques.

A fundamental responsibility of a data professional is utilizing data for precise predictions. This module focuses on building predictive models with programming languages such as Python, using past data and known outcomes from an organization. Such models can reliably foresee future occurrences, allowing organizations to develop data-driven strategies under certain situations.

Participants gain proficiency in deciphering data patterns and trends, and applying them to construct effective predictive models.

This module teaches you how to apply machine learning in real-world scenarios. Participants gain insights into the diverse applications of machine learning across various domains and industries, along with an understanding of the top Python libraries for machine learning.

The emphasis extends to many machine learning types, including supervised learning and algorithms, most notably time series forecasting. This inclusion aims to enhance forecasting accuracy while minimizing the loss function.

This module introduces unsupervised machine learning, covering concepts such as clustering, Principal Component Analysis (PCA), and Recommendation Systems.

Students learn about data interpretation approaches and how to use AI to build systems that can make correct product suggestions. These recommendations are based on previous browsing habits and user search history.

This module provides an introduction to Natural Language Processing (NLP) and Text Mining using Python, a widely used programming language.NLP is the study of the text, voice, or grammatical syntax to comprehend human language. Text mining, on the other hand, pulls information from both organized and unstructured texts, with a focus on content structure rather than meaning

Participants get an understanding of the machine learning methods used in text mining and NLP. The module also provides methods and approaches for developing data analysis algorithms in Python.

This module introduces various data analytics applications from marketing, risk, operations, and digital domains.

• Marketing: Students gain insights into evaluating marketing activities through data analysis. The module explores how businesses utilize analytical processes to assess customer-based data and devise solutions. Additionally, it covers the three major types of marketing analytics: descriptive, predictive, and prescriptive.

• Risk Management: Focusing on a highly sought-after skill set, risk analytics, participants learn to measure, assess, and manage risk through data analysis. The module prepares students to evaluate data, properly estimate business risks, discover patterns and trends, conduct competition analysis, and acquire the ability to detect and mitigate risks efficiently.

• Operations: This segment imparts skills to make informed business decisions using data. With evolving data-collecting technologies and a growing emphasis on informed decision-making, participants are prepared to leverage data for optimizing business efficiency. The module aids them in aligning data with corporate profit and supply margins, projecting outcomes, building models for future demands, managing uncertainties, and comprehending frameworks that explain the functioning of real organizations.

This elective module provides clarity on core concepts and their distinctions, elucidating the relationship between AI, Deep Learning, and Machine Learning and their impact on daily life at both micro and macro levels.

Key Learnings:

• Artificial Intelligence involves creating intelligent machines.

• Deep Learning is an advanced concept, employs complex datasets to replicate human brain thinking through artificial neural networks.

Subsequently, students delve into:

• Computer Vision: A branch of artificial intelligence that enables computers to draw meaning from digital photos, visual inputs, or videos, allowing for correct suggestions based on obtained data.

• Text Mining: Students learn NLP strategies for transforming unstructured data into structured data and detecting relevant patterns within it.

This serves as a second elective specialization option. In addition to the increase in data volume, data velocity has reached previously unheard-of levels. This module explores the realm of big data, which encompasses data exceeding the capacities of traditional storage, processing, and analysis methods.

Key Components Covered:

• Frameworks such as Hadoop, Cassandra, Apache Storm, and Spark.

• Databases like NoSQL

• Participants gain insights into handling vast amounts of data, analyzing it, and extracting meaningful insights through this module.

Under the guidance of your mentor/teacher, students will undertake the completion of their dissertation, subject to evaluation. While there is no pass/fail concept, a dissertation that is unfinished or incorrectly executed will result in program non-completion.

For a more practical approach in the final term, the Capstone Project work allows students to choose from various project options:

1. Sports Event Analysis and Reporting

2. Analysis and visualization of pricing data for consumer electronics

3. Telecom Churn Prediction (Classification & Machine Learning)

4. Credit Card Spending Prediction (Regression Methods)

5. Prediction of Peer Group and Analysis (Regression Methods)

6. Marketing & Sales Data Manipulation and Analysis

7. Data Analysis and Reporting for Airlines

8. Sports Equipment Retail Data Analysis and Visualization

This session focuses on problem-solving, which is an important ability for professional success. Under the mentor's guidance, students will develop an approach to systematically break down and structure complex problems into manageable logical steps/tasks. To secure solid business outcomes, the emphasis is on using a data-driven strategy.

Ai Campus mentors will provide individualized guidance on career development, resume refinement, job applications, and interview readiness.

We are a well-known provider of placement and interview aid, and our PG in Data Science program is no exception.

In the final module, students receive personalized guidance in preparing for interviews, developing a project portfolio, and raising their profile. Dedicated mentors and experts will help identify the most suitable job roles, guide students on the way to approach them, help prepare for interviews, and offer any necessary guidance for successful placement. Our thorough placement assistance includes the following:

• Project Portfolio

• Profile Building

• Mock interviews

• Career Counseling

• Internships / Freelance projects