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Advance Diploma in Business Analytics

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on successful completion of the course

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In today’s competitive work environment, it has become vital for every individual to continuously upgrade their knowledge and competencies to stay relevant and meet professional goals.

Organizations today, are always on the lookout for young professionals who are proficient in emerging technologies and can thereby contribute to overall business from day one.

The Diploma in Business Analytics program offered by Dygitech adds value to all participants whether they are working executives, technology professionals, entrepreneurs, industry leaders, or final year students.

Some of the most determinative reasons for you to pursue this program is because it is Industry-aligned courses to bridge this gap and accelerate their career.

Business Analytics Data

  • Instructor – Led Training

  • Live Simulated Projects

  • Dedicated Career Assistance

Course Outline

  • Introduction to Business Process and Models

    You will learn about various sources of revenue and expenses of a business. In this session important sectors like Banking & Financial Service, Retail & FMCG, Healthcare & Pharma, Construction & Heavy Engineering will be covered along with live case studies and modern theories.

  • Introduction to Business Metrics

    You will learn the industry standard metrics that incorporates various industry and business functions. Case study driven approach will augment your exposure to the leading practices in the business.

  • Introduction to Data Analysis in Excel

    This course is designed to give you a working knowledge of MS- Excel with the aim of utilising it for more advance topics in Business Statistics.

  • Introduction to Applied Business Statistics

    This session begins with the notion of descriptive statistics. Different categories of descriptive measures are introduced. The notion of probability or uncertainty is introduced along with the concept of a sample and population data using relevant business examples. This is followed by hypothesis testing and applying the concept of inferential statistics to Business.

  • Regression and Classification for Business Applications

    As a beginner in the advanced analytics, you will learn the most widely used techniques and apply them in real life scenarios from industry. This chapter will include a significant section dedicated to practical lessons.

  • Tools and Techniques of Data Visualization and Communication

    Data Visualization characterizes the skill of communicating practical implications of quantitative analysis to any kind of audience. Here you will learn to ”Ask the right Question” from inception.

  • Introduction to Data Science

    This course introduces main tools and ideas in the data scientist’s toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge.

  • Guide to R Programing

    This session introduces R programing language; the numero-uno choice of Data Scientist. The session would impart all the necessary tricks required for managing and manipulating Data in R environment. The session also introduces the participants to IDE for R, i.e., RStudio.

  • Building Data Products using Shiny R

    A data product is the output of a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages and interactive graphics.

  • Exploratory Data Analysis

    This session covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modelling commences and can help develop more complex statistical models.

  • Managing Data With MySQL – Building block of Business Intelligence

    This course is an introduction on how to use relational databases in business analysis. We shall use SQL syntax to extract and manipulate data in the database. Analysts who understand how to access this data – (this means you!) – will have a strong competitive advantage in this data-smitten business world.

  • Putting it together – Walk through an end-to-end Data Science Project

    In this session we shall perform all the steps of Data Science which are required to solve the ‘right’ Business problem. It will start with ‘Asking the right question’ and then follow the steps to build and deploy a robust model for prediction. You shall be using Shiny R along with other necessary R tools. While data sets will be given in advance in a specified format, it is you who will turn them into actionable insights.

  • Practical Machine Learning

    This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications using machine learning techniques. In this class you will get a broad introduction to machine learning, data mining, and statistical pattern recognition.

    1. Supervised learning (parametric/non-parametric algorithms, support vector machines).
    2. Unsupervised learning (clustering, Apriori Algorithm, dimensionality reduction).
    3. Best practices in machine learning (bias/variance theory, innovation process in machine learning and AI).
  • Introduction to Deep Learning Techniques

    Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is the study of artificial neural networks and related machine learning algorithms that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning representations of data. In this session, we shall introduce the neural net and how they can be used in detecting images.

“India will face a demand- supply gap of 2 million Analytics professionals over next three years”
– Team Lease

“Global studies by McKinsey confirmed that there will be a serious dearth of data scientists by 2018 across the world.”

While analytics courses are aplenty, gaining enough industry exposure as part of the course helps the student become more employable. One more study stresses that academic courses need more involvement of of the industry. Dygitech would be the right choice for young learners to gain deep insights into industry processes and applicability of analytical concepts in practical scenarios.

Admission Guidelines

The candidate must be a graduate or its equivalent from a recognized University with Physics or Mathematics and English in one sitting, securing a minimum of 45% marks in aggregate.

Mode of Selection

  • Students willing to take admission will have to submit a duly filled in application form downloading it from the site
  • Have to clear the written and viva test conducted by Dygitech
  • A merit list shall be prepared and candidates will be called for counseling, as per merit list
  • The selected candidates will have to pay the first semester fees at the time of admission
  • Mode of payment is by cash or by a Demand draft
  • Aspiring candidate can also get admitted on the spot in the Counseling Session

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