MANAGEMENT ACADEMY
FOR DIGITAL ECONOMY
IN INDIA

(Approved by the Govt of India. Govt of Karnataka. ISO 9000-2015)

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Data Science

Data Science

Data Science is one of the most sought after skills in today’s world. Thisknowledge stream uses scientific methods, processing, algorithms, and problem-solving oriented subject. In short, Data Science is a combination of data mining and computer sciences. It is the study of the impact of data on the company / organization / industry / situation. It is an evolution of the web design arriving at a sophisticated data infrastructure. Websites have evolved from simply displaying pamphlets and brochures to a more interactive system by employing and handling relevant data.

It requires technical knowledge of various coding languages and awareness of some technologies but the central element of data science is not mere coding or using complicated technologies to make live or visual models. To be a successful data scientist, in-depth knowledge of data structure and data manipulation is necessary. Critical skills including logical, statistical and technical education become equally important. A decent background in software languages like R, Python, SQL, etc. would be an added advantage.

The Data Science is not just about the assimilation of raw data and structuring it, but also analyzing data that can be both structured and unstructured. Various tools and algorithms are taught through the course that help in a better understanding of data and therefore, understand the predictive analysis aspect of Data Science. The skill of finding out and decoding raw data becomes instrumental as it helps organizations to make big decisions and impacts their output.

3 Key Components of Data Science

Machine Learning

Machine learning involves mathem aticaland algorithm models. The main aim is to develop in the students to machine learn and adapt to everyday advancement. The machine can predict future outcomes from the historical data patterns.

Big Data

The data which is generally unstructured is called Big Data. For example- images, videos, orders, comments, articles, etc. The unstructured data is then converted into a structured form by using big data tools and techniques.

Business Intelligence

There is too much data that is produced by each business. This data is then presented in a visual presentation form. This will help the management to make the best decision.

Highlights of the program

  • Focus on applying data science in practical business.
  • This Data Science course will help you develop better business design.
  • You become adept at making better business strategy for your company.
  • This course will help you gain detailed insight via data visualization.
  • You will take your company ahead in the competition.
  • This Data Science course helps you maintain your competitive edge in career planning.

Learning from the Program

  • Data Science aims to develop practical data analysis skills.
  • Data Science develops knowledge of concepts of fundamental skills.
  • Data Science develops the modern concepts of technology.
  • Data Science explains the contribution of maths and information science in building algorithms and software.
  • Data science gives real-world practical experience.
  • Data science involves analytical skills development.
  • Data science classes consist of independent mini-projects performed by the students.

Career Scope of a Data Scientist

Data scientists are scarce in the market currently and in much demand all over the world. Therefore, a course in data science is sure to help you build an excellent global career. Apart from being HEROES OF THE IT INDUSTRY, a data scientist can excel in the healthcare industry, travel industry, financial institutions, food industry and many more. Industries handling huge amounts of data are in constant need of data scientists. Data can never be permanent. So, this change keeps the role of data scientists active at all generations.

Future in Data Science

Data scientists will continue to remain in high demand for many more decades because data structuring cannot be eliminated from any business process. An alternative method or system as efficient as data science may not be easy to be replaced.

Job description for Data Science

A data scientist is expected to understand and analyze the business problem, develop a strategy by collecting required data and format it using algorithms or techniques employing appropriate tools and finally make recommendations for solving the issue. All allegations kept forward need to be backed by suitable data. Data Scientists command their own salaries in the market.

Business Intelligence (BI) Developer

Typical Job Requirements: BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Extremely data-savvy, they use BI tools or develop custom BI analytic applications to facilitate the end-users’ understanding of their systems

Data Architect

Typical Job Requirements: Ensure data solutions are built for performance and design analytics applications for multiple platforms.

Applications Architect

Typical Job Requirements: Track the behavior of applications used within a business and how they interact with each other and with users.

Infrastructure Architect

Typical Job Requirements: Oversee that all business systems are working at optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which oversees a company’s cloud computing strategy.

Machine Learning Scientist

Typical Job Requirements: Research new data approaches and algorithms.

Machine Learning Engineer

Typical Job Requirements: Create data funnels and deliver software solutions.

Enterprise Architect

Typical Job Requirements: According to Techopedia, an enterprise architect, “Works closely with stakeholders, including management and subject matter experts (SME), to develop a view of an organization’s strategy, information, processes and IT assets.”

Data Scientist

Typical Job Requirements: Find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions. Compared to data analysts, data scientists are
much more technical.

Data Analyst

Typical Job Requirements: Transform and manipulate large data sets to suit the desired analysis for companies. For many companies, this role can also include tracking web analytics and analyzing A/B testing.

Data Engineer

Typical Job Requirements: Perform batch processing or real-time processing on gathered and stored data. Make data readable for data scientists.

Statistician

Typical Job Requirements: Interpret, analyze, and report statistical information, such as formulas and data for business purposes.

DATA SCIENCE

Course Curriculum

Introduction & Importance of Data Science

Introduction and Importance- Data science has gained importance in the business world. In this subject, you will teach about the handling of data which is in the unstructured form to convert it into a structured form.

Probability & Statistics

Discover what the data is conveying or implying by analyzing it. This is the core part of decision-making. Careful analysis of data to arrive at meaningful solutions to business problems should be done which is achieved by the following systems.

Basic mathematics used for the analysis of data like mean, median, mode, standard deviation, variance (measures of average and dispersion). Probability distributions like Poisson distribution, binomial distribution, etc. Application of various theorems and equations to manipulate the data according to the case (ex: through linear transformation) Calculation of deviation of data from the standardized or normalized value through curve analysis.

Mathematics for data analytics

  • Linear Algebra for Data Science. Matrix algebra and eigenvalues.
  • Calculus for Data Science. Derivatives and gradients.
  • Gradient Descent from Scratch. Implement a simple neural network from scratch.

Programming

All data precisely referred, as big data cannot be jotted down on paper or excel sheets every time. It becomes impossible to handle data when it pars a certain fixed volume. This invites the need for programming languages to fetch a few particular sets of data to be analyzed or manipulated through selection criteria by writing relevant codes.

TECHNOLOGIES FOR DATA SCIENCE : Python, R, and SaaS are the most commonly used programming languages in data science.

At MADE ACADEMY you also learn the latest Jupyter Notebook, Tableau, Keras. Python and Jupyter notebooks are languages best suited for a data scientist and are powerful open-source languages. An applied understanding of how to manipulate and analyze uncurated datasets. Fundamental statistical analysis and machine learning methods. Visualization of effective results. This will prepare you for three main courses
* Statistics *Machine learning *ApacheSpark

R Software –
R is an open-source programming and statistical language. It is a very well documented software language thus being easy for learning. It is highly cost-effective and has strong statistical capabilities.

SaaS –

SaaS (Software as a Service) is a Statistical Analytical System designed for performing statistical operations conveniently. The commercial analytics market uses the Saas tool prominently. It comes with an excellent GUI. Being a fourth-generation language, it is suitably designed to accommodate operations in the development of commercial business software. It imbibes many in-built codes in order to reduce coding time and effort. It stands unique from Python and R by being a fourth-generation language.

Database knowledge

Data Mining, Data Structures, and Data Manipulation : programming language is used to fetch and work on the stored data, the data has to be stored somewhere. It obviously cannot fit into the office tools and hence employment of databases comes into the picture. All companies involving data science sectors in their projects make use of MySQL or Cassandra (NoSQL) databases. The gathered or keyed in or transmitted information is stored in simple and complex tables with unique properties.

Machine learning and deep learning

Even when all the data and databases are in place along with statistical analysis, these remain untapped without machine learning. In simple terms, machine language is nothing but a conversion of the human-understandable data into machine interpret table code values. The machine can understand these codes and not explicit programming. This is achieved using algorithms and sometimes artificial intelligence (AI). Deep learning is a subdivision of machine learning which is more specific and applies algorithms more independently and with much lesser inputs from the user or programmer. It is built with artificial neural networks and is more prompting by recording data retrieval history by experience. This particular subject focusses on and expects you to be well informed of the different algorithms like linear regression, clustering, logical
regression, decision trees, etc. The connection between neural networks, libraries and algorithms are studied here.

Big Data Technology

All the leading industries handle big data today and require analytics. Big data technologies help to write map produces codes. The most prevalent big data technologies are Hadoop HDFS and Apache Spark

  • Data ingestion and Data Munging:
    In data ingestion, data is imported or received through proper channels for being manipulated or analyzed or stored. There are tools for this process – Apache Flume and Apache Corp. Any change that is made using artificial algorithms upon raw data for clearer visualization is data munging. R and Python packages may be used for this. Selection and removal of data are done through analysis and any data discrepancies are corrected.
  • Visualization:
    The most important part is to organize the analyzed data and present it. No technology or algorithm or code can be shown. An understandable report needs to be submitted. Report creation training and techniques will be learned under this sub-topic.
  • Problem-solving:
    The ultimate purpose of the entire course is to solve the business problem using data sets. The examined characteristics of the given raw data need to be taken into account to make a final call over the problem. For example, if a certain value in the data is high then the impact of the deviation is analyzed and arrived at a perfect strategical solution to overcome the variance. Hands-on experiences and case studies will be given in the problem-solving section under the data science course.
PRACTICUM : *Data Analytics Lab *Advanced Data Analytics Lab

Team Members

PROF H. V DINESH PRASAD

M.Sc, MBA

DR. PROF. M R SRIPATHI RAO

Ph.D, MBA,MCom, PGDIM

MS. AMITHA PRASAD

BCA, MBA (Fin & Stats)

DEVATHA HNG

MBA, PGDM, MA EDU. MSWC

Dr.JAY BHARATHEESH SIMHA

BE, M.Sc, Ph.D

BHAGYASHREE KULKARNI

Data Scientist, SAP (Siemens Certified)

KAVITHA A

MBA

PROF SUDHEENDRA M

M.tech, (MBA)

DR. PRAVEEN M P

Ph.D, M. Tech, BE

KANCHAN KUMAR BHOWMIK

Data Scientist

KIRAN GURURAJ

PROF. MUZAMMIL AHMED

MBA
BOOT CAMP Data Scientist Roles and Responsibilities
  • Data Acquisition and Data Science Life Cycle
  • Deploying Recommender Systems on Real-World Data Sets
  • Experimentation, Evaluation and Project Deployment Tools
  • Predictive Analytics and Segmentation using Clustering
  • Applied Mathematics and Informatics

Advanced Specialization

Combinations of data science with various other disciplines

AI + Data Science

This combo is a very powerful tool in the industry since the entire world revolves around using artificial intelligence to a high degree right from mobile phones to detectors Programs are offered by many universities for this combination.

Data science + Business Analytics

Analytics and data science are bound to go hand in hand with each other because of the complimentary relationship they possess. Therefore, studying it together ensures higher success in the field. Data mining along with statistics plays an important role here because it involves the examination of large preexisting databases to generate new data. In simple terms, the conversion of raw data into useful information is used for making further decisions.

What makes DATA SCIENCE course at MADE ACADEMY important?

  • Focus on applying data science in practical business.
  • This Data Science course will help you develop better business design.
  • You become adept at making better business strategy for your company.
  • This course will help you gain detailed insight via data visualization.
  • You will take your company ahead in the competition.
  • This Data Science course helps you maintain your competitive edge in career planning.

Who Should Take This Program

  • Freshers who aspire to make data science a career option
  • Entrepreneurs desirous of creating their own sytartup
  • Professionals who intend to make their business operation more efficient
  • Business professionals who look to improve profitability of their business
  • Professional who wish to add value to their business

Eligibility Criteria

Academic qualification
  • Minimum 50% in GRADUATE DEGREE B E, B. Tech.
  • Other degree with 50% marks with maths, statistics, economics and knowledge of computer basics.
  • Work experience of 1-2 years preferred.
Application Process

Fill up the Application Form through
the link provided for Online Application
Apply Now

POST COMPLETION SUPPORT:
MADE will provide following support services for our participants

  • 100% Guaranteed Placement: We will ensure your placement in MNC’s, Mid & Small Size Companies or Ecommerce portals
  • Resume Support: Resume makes the first impression. At MADE ACADEMY you can curate a customized resume with the support of experts to make your first impression the best one
  • Interview Questions: Team of experts shall equip you with a set of probable interview questions and answers to face the interviews confidently
  • Mock Interviews: As part of Action Learning our experts will help you to increase your placement success by practicing numerous mock interview sessions
  • Job Updates: MADE career planning team shall post all latest job updates
  • Help in setting up your own business

Prospective Placement Providers

CERTIFICATION

Earn a Certificate of Post Graduate Diploma in Business Analytics from the MANAGEMENT ACADEMY FOR DIGITAL ECONOMY IN INDIA endorsed by the industry and Govt agency.

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