Data science is an interdisciplinary field that involves using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data science is a combination of several fields, including statistics, computer science, and domain-specific expertise. It involves the use of various techniques such as machine learning, deep learning, natural language processing, and more to analyze and interpret complex data sets.

The goal of data science is to extract meaningful insights and knowledge from data, which can be used to inform decision making, create new products and services, and improve existing ones. Data scientists use a wide variety of tools and techniques to collect, store, process, and analyze data, such as programming languages like Python and R, SQL and NoSQL databases, big data technologies like Hadoop and Spark, and visualization tools like Tableau and Matplotlib.

Data science can be applied in a wide range of industries and fields, such as healthcare, finance, marketing, transportation, and more. Its applications can include everything from developing predictive models to improve medical diagnoses, to optimizing supply chain logistics, to personalizing customer experiences in e-commerce and streaming platforms.

One of the important works of Data Scientists is to find actionable insights from data. They are able to extract information and extract meaningful insights from data and turn them into strategies and solutions that can be implemented in the business

Data Scientists not only analyze data to gain insights, but they also use those insights to create real-world data products. These products can take many forms, such as chatbots, conversational AI systems, AI-based proctoring systems for online exams, personalized ad systems on social media platforms, and recommendation systems for e-commerce (Flipkart, Amazon) and streaming services (Netflix)

In addition to creating data products, Data Scientists also use their insights to develop AI products, which can be used to automate various tasks and improve overall efficiency.

• They find actionable insight from the data
• They develop Data and AI products

Business analytics is the use of data, statistical and quantitative analysis, and fact-based decision-making to inform and guide business decisions. It aims to improve business performance by identifying trends, predicting future outcomes and making data-driven decisions using data visualization, reporting and statistical analysis methods like descriptive, diagnostic, predictive and prescriptive analytics. Overall, business analytics helps organizations extract value from data, leading to a better understanding of their business and better decision-making

• Focus: Data Scientists tend to have a more technical focus, while Business Analysts have a more business-oriented focus
• Tools and techniques: Data Scientists use advanced techniques and tools such as machine learning and artificial intelligence to analyze data, while Business Analysts may use more basic tools such as Excel and SQL.
• Responsibilities: Data Scientists may be responsible for developing data-driven products and solutions, while Business Analysts may be responsible for creating reports and dashboards, identifying trends and patterns in data, and developing strategies to help the business reach its goals.

Big Data refers to large and complex datasets that traditional data processing methods are unable to process and manage efficiently. These datasets typically have high volume, velocity, and variety of data. The high volume of data is generated from various sources such as social media, online transactions, sensors, and devices. The data is generated and collected at a high velocity, which is difficult to process using traditional methods in near real-time. The data also has a wide variety of formats such as text, images, videos, and audio.

The growing amount of data, combined with the need for more accurate and actionable insights, has led to the development of big data technologies and methodologies. These include distributed storage and processing systems such as Hadoop, and analytical tools such as Spark and Hive, as well as machine learning algorithms and NoSQL databases.

AI plays a vital role in data science by providing advanced techniques and tools that can process and analyze large amounts of data in a way that is similar to how a human would, but at a much faster pace and with greater accuracy. AI-powered methods such as machine learning, deep learning and natural language processing can be used to extract insights and make predictions that would be difficult or impossible for humans to detect or discern manually. Additionally, AI can be used to automate tasks such as data cleaning, feature extraction, and model selection, which can make the data science process more efficient and streamlined.

Some of the key skills required to become a data scientist include:

• Strong analytical and problem-solving skills: Data scientists need to be able to analyze data and solve problems using mathematical and statistical techniques
• Programming skills: Data scientists should have proficiency in one or more programming languages, such as Python, R, or SQL, for data cleaning, manipulation and modeling.
• Machine Learning: Understanding of machine learning concepts and algorithms, including supervised and unsupervised learning, and ability to implement them is crucial for data scientists.
• Strong mathematical background: Understanding of statistical concepts, linear algebra, and calculus is important for data scientists as it is required for implementing various machine learning algorithms.
• Data wrangling: Experience with data cleaning, feature engineering, and preprocessing is important, as data scientists often have to work with messy and incomplete datasets.
• Understanding of databases and data warehousing concepts: Data scientists should have knowledge of databases, including proficiency in SQL, and experience with both relational and non-relational databases.
• Strong communication and presentation skills: Data scientists need to be able to explain technical concepts to non-technical stakeholders and present data-driven insights in a clear and compelling way.
• Cloud computing and big data technologies: Data scientists should have a solid understanding of cloud computing, big data technologies and distributed computing frameworks like Hadoop, Spark, etc.
• Business acumen: Data scientists should have a good understanding of the business context in which they are working, and be able to apply their skills to real-world problems
• Data visualization: Data scientists need to be able to present complex data in a way that is easy for non-technical stakeholders to understand. They should have experience using data visualization tools such as Tableau or Power BI.

Overall, a data scientist should have a combination of analytical, technical, and business skills, and should be able to apply their knowledge to real-world problems. Additionally, a data scientist should be comfortable with ambiguity, open to learning new things, and able to stay up-to-date with the latest tools and technologies.

The requirement for skills can vary depending on the specific role and organization, but possessing these skills will help a data scientist become well-rounded and versatile in their field.

Aegis School of Data Science, Business & Telecommunication is an institution located in Mumbai, India that provides education, training and consulting in various fields related to technology and data science, such as data science, analytics, big data, AI, telecom, cyber security, and technology management. It was established in 2002, with the support of Bharti Airtel, with a goal of developing cross-functional techno leaders.

In 2015, Aegis joined hands with IBM and launched India’s first Post-Graduate Program in Data Science, Business Analytics, and AI (PGP DS-BA-BD) which is considered as one of the top programs in the country. This program is designed to train the new generation of data-savvy professionals and provides students with intensive hands-on training to develop a unique set of skills required for a successful career in data science.

Aegis also initiated one of the most respected innovation award, the Aegis Graham Bell Awards in 2010 which recognizes innovation in various fields of techology. The Institute also hosts the Data Science Congress, one of the largest events in the Asia-Pacific region, which is a platform for showcasing the latest methods, tools, and best practices in the field of data science.

The Post Graduate Program in Data Science, Business Analytics, and AI is a 11 month, Full Time intensive program that covers a range of topics including data science, business analytics, machine learning, artificial intelligence, and more. The program is designed to give students the skills and knowledge needed for a successful career in these fields.

Since 2015, hundreds of Aegis data science graduates of over 40 batches (19 full-time and 22+ weekend) have already launched their careers successfully as data scientists, AI engineers, Business Analysts, etc with a min of 8.5 lacs to 40 lacs package and up to 500%salary hike on the last package. They are working with500+ leading corporate & startups like Reliance, Deloitte, E&Y, Redhat, Vodafone, Lenovo, Shaadi.com, etc

Aegis is teaching Data Science, Business Analytics, and AI together in one program because these fields are closely related and interdependent

Data Science provides the methods and techniques for extracting insights and knowledge from data, while Business Analytics applies these methods to analyze data and make informed business decisions. AI plays a crucial role in Data Science and Business Analytics by enabling the automation of data analysis, pattern recognition, and predictive modeling, ultimately providing actionable insights and informed decision-making

By teaching these fields together, students will have a comprehensive understanding of how to work with large and complex datasets and extract insights from them. They will learn to use the data science techniques such as statistics, machine learning, and artificial intelligence and apply these techniques using the big data technologies such as Hadoop and Spark, and business analytical tools to analyze the data and make informed business decisions.

Additionally, having a holistic view of these related fields allows students to better understand the end-to-end process of data-driven decision making which is in-demand in today's industry. They will be able to apply the knowledge and skills learned in class to solve real-world problems and make an impact in their chosen field.

No, the PGP in Data Science, Business Analytics, and AI is a certificate program. It follows a 45 credit unit structure, which is the typical length of a master's degree program in North America.

The duration of the program is 11 months.

Yes, there is a full-time online option available for the program. This option allows students to complete the program entirely online, without the need to travel to our boot camp training facilities.

The course structure for the program includes a mix of lectures, hands-on training, and internships. It covers topics such as data science, business analytics, machine learning, and more

To be eligible for the program, candidates must have a bachelor's degree in any discipline with a minimum of 50% marks. They must also have strong analytical and problem-solving skills.

To apply for the program, candidates must fill out an application form and submit it along with the required documents. Applications can be submitted online at municampus.com

The selection process for the program includes an online aptitude test, followed by an interview with the admissions committee. Candidates who pass the aptitude test and interview will be offered a place in the program.

To prepare for the Aegis Aptitude Test, candidates should brush up on their knowledge of general mathematics, logical reasoning, and data interpretation. They can do this by reviewing relevant materials and practicing with sample questions or practice tests.

There will be one round of interview for the Aegis Post Graduate Program in Data Science, Business Analytics, and AI.

During an interview for a data science, analytics, AI, or ML program, candidates can expect to be evaluated on their logical and analytical reasoning abilities, learnability, personality, motivation, and understanding of data science, analytics, AI, and ML. The interviewers will assess the candidate's objectives and passion for using their knowledge in these fields, their readiness to learn and adapt to new developments and their understanding of the different areas of focus within these fields and the different applications of these concepts.

For online interviews, candidates do not need to bring any documents. However, if they are shortlisted for the program and confirm their admission, they will need to bring the required documents at the time of registration. For offline interviews, candidates should bring the original copies of their mark sheets, experience certificates, ID proof, and resume, as well as self-attested photocopies of these documents.

If selected for the program, the candidate will receive a conditional offer of admission. To officially accept the offer and secure their spot in the program, they must pay the confirmation fee and submit a completed acceptance letter. The confirmation fee is a partial payment towards the total tuition fee and upon payment and acceptance letter submission, the candidate will receive a final confirmation of their admission.

To get in touch with the admissions team at Aegis, candidates can visit the Aegis website and fill out the contact form, or send an email to admission@aegis.edu.in. They can also call the admissions team at +91-22-28599900/ +91 9022137010