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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study, Virtual Classroom	Video and Text Based

Course Overview

As you know, data is at the centre of every successful organisation. It can even be referred to as the ‘heart’ of a company. Thus, the role of data professionals is pivotal to the growth and business decisions taken by any organisation. And the Certificate Programme in Data Science by Eruditus will teach you analytical techniques to treat data and solve business problems.

Besides, according to the World Economic Forum, skills will become much more crucial than job titles by 2022. This means it is critical to upskill yourself and learn some in-demand skills. Moreover, the Certificate Programme in Data Science online programme follows a self-paced format to help you learn with convenience.

The Certificate Programme in Data Science curriculum will outline the essence of data, the processes involved in a data cycle, descriptive, predictive and prescriptive analysis, and more. The Certificate Programme in Data Science classes will contain the combination of live webinars and video lessons. What’s more, you can receive a completion certificate for the Data Science online course from one of the best B-schools in India.

The Highlights

3-month duration
Certificate of completion from one of the top B-Schools in India (NIRF, 2020)
Personalised career services
Live webinars
Hands-on experience
Doubt clarification with industry experts

Programme Offerings

LIVE Webinars
Personalised career services
Hands-on Experience
Certificate of completion
Doubt clarification with industry experts

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIM Kozhikode

Eruditus allows you to pay the Certificate Programme in Data Science fee through different methods. You can either pay the whole fee upfront or through a standard or no-cost EMI plan.

Certificate Programme in Data Science fee structure

Course Name	Fees
Program fee	Rs. 71,750 + GST

Eligibility Criteria

To pursue Data Science online training, you must have completed 10+2+3 years of education. Hence, you must either have a Graduate degree or a 3-year Diploma in any discipline along with work experience of 1 year.

What you will learn

Data science knowledge

Eruditus’ Data Science syllabus includes the following:

The importance of data in making vital business decisions
The people and processes involved in a data cycle
The use of predictive, descriptive, and prescriptive analytics to drive growth
The spectrum of data analytics in making business decisions
Using data analytics to draw conclusions and extract insights from datasets
Understanding the concept of hierarchical clustering
Hands-on experience with Python and MS Excel

Who it is for

The Data Science certificate programme has been designed for working professionals in any field who wish to advance their careers.

Admission Details

Step 1: Land on the Certificate Programme in Data Science webpage.

Step 2: Click on ‘Start Application Now’ and provide your email address to proceed. Verify your email by entering the OTP.

Step 3: Fill the application form with your personal information, Aadhaar number, country, contact information, address, nationality, etc., and save the data.

Step 4: Enter your employment details, SOP, upload your documents and submit the form.

Application Details

The Certificate Programme in Data Science online course by Eruditus involves an application form in its enrolment process. The enrolment form is split into different parts, and you must fill in all the information to proceed. You must enter your personal information in the first part along with your Aadhaar number, age, country, residential address, contact info, nationality etc.

You must save your information and then proceed to the employment details. After that, you can choose to skip the SOP as it is optional and then upload your documents before submitting the form.

The Syllabus

Outline the importance of data in making business decision
Describe types of data and data catagories
Describe the people and processes involved in data cycle
Compare the characteristics of small data and big data
Discuss the importance of data analytic for business decision making
Identify actions taken during the cross industry standard process of data mining
Discuss the data science links with other discipline

Describe the benefits of using Microsoft Excel for making data-driven decisions
Calculate stastical averages using Microsoft Excel functions
Apply basic Microsoft Excel tools for data analysis
Perform different analysis on data using techniques like what-if analysis, goal seek analysis, sensitivity analysis
Use filter function to remove duplicates and calculate subtotals

Review and delineate the evolution and purpose of Python
Describe and set up Python development environment
Practice coding with basic Python commands, operators and conditional statements
Explore and apply Python data structure concepts such as array, list, tuple, set and dictionary
Import python modules and packages
Import Python libraries such as NumPy, Pandas

Describe the need of data preparation
Describe the sources of data
Evaluate and improve quality of data
Differentiate between hypothesis testing and exploratory data analysis
Explore categorical variables

Describe spectrum of business analytics
Describe application of descriptive analytics
Draw conclusions from a given set of data by using descriptive analytic techniques
Describe application of diagnostics analytics
Draw conclusions from a given set of data by using diagnostics analytic techniques
Describe application of predictive analytics
Draw conclusions from a given set of data by using predictive analytic techniques
Describe application of prescriptive analytics
Draw conclusions from a given set of data by using prescriptive analytic techniques

Discuss Cross Industry Standard Process in Data Modeling
Discuss a Generic data modeling process
Apply prior knowledge the address the business problems

Discuss overfitting modeling
Explain data driven modeling
Describe decision tree and its types
Design a classification tree to resolve uncertainties

Describe concepts of clustering and visualize data
Apply K-means algorithm to cluster the data
Apply Z-score method to standardise the data
Interpret the cluster centre and create product segment
Use Dendrogram and Elbow Curve for estimating the number of clusters
Estimate the quality of clustering using Silhouette scores

Explain the limitations of K-means clustering
Apply hierarchical clustering to the product segmentation and the Gaussian distributed dataset
Describe the DBSCAN clustering technique and its benefits
Apply K-Means, Hierarchical and DBSCAN clustering to the moon dataset
Discuss the limitations of clustering algorithms and techniques to address them

Discuss correlation and its characteristics
Perform association analysis by generating general association rules between market variables
Apply association rule to restrict frequently appearing data items
Apply association rule to identify frequency of conditional probability