Diploma in Data Science

BY
DataMites

Enrol in the Diploma in Data Science course by DataMites to gain in-depth data science knowledge, acquire in-demand skills, and give a boost to your career.

Mode

Online

Duration

3 Months

Fees

₹ 42948 52000

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom +1 more
Mode of Delivery Video and Text Based

Course overview

The Diploma in Data Science programme is a hands-on course in data science and machine learning, developed chiefly for fresh graduates and professionals. This is a meticulously curated training course that will provide foundational knowledge of interpreting data, skills, and abilities to extract information, analyse, transform, and model data. 

The Diploma in Data Science online course offers a unique blend of expert-designed curriculum, structured classroom learning, and hands-on labs, concluding with a final real-time data science project. In addition to the capstone, the course features one client project as well. You also get the option to choose whether you want in-person training, online training, or blended learning.

After completing the Diploma in Data Science training, you also receive a certificate signed by both DMC and IABAC. Moreover, you also receive access to the cloud lab, where you can practice all the techniques learnt during the course. The certificate, combined with the internship and job assistance and hands-on training, will act as a launchpad for your career. 

The highlights

  • 3-month course
  • Internship + Job assistance
  • DMC and IABAC certification
  • 5 capstone projects
  • Unlimited access to Data Science Cloud Lab
  • 1 client project
  • Classroom training
  • Live project mentoring
  • Self-paced learning
  • Blended learning
  • In-classroom learning

Program offerings

  • Job assistance
  • In-person training
  • Live mentoring
  • Self-paced learning
  • Iabac and dmc certification
  • Internship
  • Capstone projects
  • Client projects
  • Expert faculty

Course and certificate fees

Fees information
₹ 42,948  ₹52,000
  • The Diploma in Data Science live programme offers three enrolment options for students. 
  • You can either opt for the Live Virtual training, Blended, or Classroom option. 
  • The fee is different for all three choices.

Diploma in Data Science fee structure

Mode of learningTotal FeeDiscounted Fee
Live Virtual Rs. 52,000Rs. 42,948
Blended Learning Rs. 36,000
Rs. 20,398
Classroom Rs. 65,000
Rs. 47,048
certificate availability

Yes

certificate providing authority

IABAC

Who it is for

The Diploma in Data Science online programme is ideal for early-career professionals and fresh graduates who’re looking to enter the field of data science.

Eligibility criteria

To obtain the Diploma in Data Science certification by IABAC and DMC, you have to appear for and pass the certification exam.

What you will learn

Machine learning Knowledge of deep learning Knowledge of artificial intelligence Sql knowledge

After completing the Diploma in Data Science syllabus, you will:

  • Have a thorough various data science concepts such as StatisticsTableauMachine Learning, Time series foundation, Deep Learning, and Data Science business concepts
  • Understand key concepts of statistics
  • Deliver end-to-end data science project to a client
  • Gain a better understanding of the complete Data Science project workflow
  • Exposure to real-life case scenarios through hands-on Industry-related projects
  • Ability to perform Model Deployment independently
  • Comprehensive knowledge of Machine Learning 

The syllabus

Course 1: Python for Data Science

Module 1 - Introduction to Data Science with Python
  • Installing Python
  • Programming basics
  • Native Data types
Module 2 - Python Basics: Basic Syntax, Data Structures
  • Data objects
  • Math
  • Comparison Operators
  • Condition Statements
  • Loops
  • Lists
  • Tuples
  • Sets
  • Dicts
  • Functions
Module 3 - Numpy Package
  • Numpy overview
  • Array
  • Selecting Data
  • Slicing
  • Iterating
  • Manuplications
  • Stacking
  • Splitting Arrays
  • Functions
Module 4 - Pandas Package
  • Pandas overview
  • Series and DataFrame
  • Manuplication
Module 5 - Python Advanced: Data Mugging with Pandas
  • Histogramming
  • Grouping
  • Aggregation
  • Treating Missing Values
  • Removing Duplicates
  • Transforming data
Module 6 - Python Advanced: Visualisation with MatPlotLib
  • Importing MatPlotLib & Seaborn Libraries
  • Creating basic chart : Line Chart, Bar Charts and Pie Charts
  • Ploting from Pandas object
  • Saving a plot
  • Object Oriented Plotting : Setting axes limits and ticks
  • Multiple Plots
  • Plot Formatting : Custom Lines, Markers, Labels, Annotations, Colors
  • Satistical Plots with Seaborn
Module 7 - Exploratory Data Analysis
  • Data Cleaning
  • Data Wrangling
Module 8 - Exploratory Data Analysis: Case Study

Course 2: SQL for Data Query

Module 1: SQL and RDBMS introduction
  • Basics of SQL
  • Essential commands to create and manage DB
Course 2: SQL for Data Query
  • Retrieve data from SQL database through complex select queries

Module 3: Connecting Tables in DataBase Query
  • Left Join
  • Right Join and Inner Join
Module 4: Python SQL query to retrieve from any SQL database
Module 5: Hands-On Project
  • Project to retrieve data from live SQL servers with queries as per the data requirements, in line with Data Science projects

Course 3: Hadoop - Big Data Foundation

Module 1: Introduction to Big Data
  • What is Big Data?
  • Why we need it?
Module 2: Big Data Concepts
  • Core concepts of Big Data

Module 3: Hadoop Installation and configuration
  • Hadoop Installation on various platforms.
Module 4: Hadoop – Simple use case deployment
  • Simple use-case with Hadoop

Course 4: Data Science Foundation

Module 1: Data Science Introduction
  • What is Data Science?
  • Evolution of Data Science
Module 2: Data Mining vs Business Analytics vs Data Science
  • Difference between popular terminologies

Module 3: Classification of Business Analytics
  • Descriptive
  • Predictive
  • Discovery and Prescriptive Analytics
Module 4: Artificial Intelligence vs Machine Learning
  • Basic differences in AI and ML usage

Module 5: Types of Machine Learning
  • Various Machine Learning methods

Module 6: Data Science Project Work Flow
  • 6-step Process of Data Science projects

Module 7: Industry application of Data Science solutions
  • Popular Industry applications of Data Science

Course 5: Statistics for Data Science

Module 1: Introduction to Statistics
  • Descriptive and Inferential Statistics.
  • Definitions , terms, types of data
Module 2: Harnessing Data
  • Types of Sampling Data.
  • Simple random sampling, Stratified, Cluster sampling. Sampling error.
Module 3: Exploratory Analysis
  • Mean, Median and Mode, Data variability, Standard deviation, Z-score, Outliers

Module 4: Distributions
  • Normal Distribution, Central Limit Theorem, Histogram, Normalisation, Normality tests, skewness, Kurtosis

Module 5: Hypothesis & computational Techniques
  • Hypothesis Testing, Null Hypothesis, P-value, Type I & II error, parametric testing, t-tests, ANOVA test, non-parametric testing

Module 6: Correlation & Regression

Course 6: Data Engineering with Pandas

Module 1: Introduction to Pandas
  • Pandas import
  • Basic structure
Module 2: Series and DataFrame data structures
  • The core data structure in Pandas Series and DataFrame

Module 3: Essential functions in Pandas for data mugging
  • Basic Pandas functions
Module 4: Various Data Treatment Techniques
  • Missing values
  • Duplicates
  • outliers etc.,
Module 5: Exploratory Data Analysis with Pandas
  • EDA for open datasets with Pandas

Module 6: Plotting with Pandas
  • Pandas plot function in detail

Module 7: Transformation data to get it ready for Machine Learning
  • Data treatment with Pandas introduction

Course 7: Machine Learning Associate

Module 1: Machine Learning Introduction
  • What is ML?
  • ML vs AI
  • ML workflow
  • Statistical modeling of ML
  • Application of ML
Module 2: Machine Learning Algorithms
  • Popular ML algorithms
  • Clustering
  • Classification and Regression
  • Supervised vs Unsupervised
  • Choice of ML
Module 3: Supervised Learning
  • Simple and Multiple Linear regression
  • KNN, and more
Module 4: Linear Regression and Logistic Regression
  • Theory of Linear regression
  • Hands on with use cases
Module 5: K-Nearest Neighbour (KNN)
  • Theory of KNN
  • Hands on with use cases
Module 6: Decision Tree
  • Theory of Decision Tree
  • Hands on with use cases
Module 7: Naïve Bayes Classifier
  • Bayes Theorem
  • Hands on Naïve Bayes implementation
Module 8: Unsupervised Learning
  • K-means Clustering

Course 8: Machine Learning Expert

Module 1: Advanced Machine Learning Concepts
  • uning with Hyper parameters.
  • Popular ML algorithms, clustering, classification and regression, supervised vs unsupervised.
  • Choice of ML
Module 2: Random Forest – Ensemble
  • Ensemble theory, random forest tuning

Module 3: Support Vector Machine (SVM)
  • Simple and Multiple Linear regression
  • KNN
Module 4: Natural Language Processing (NLP)
  • Text Processing with Vectorization
  • Sentiment analysis with TextBlob
  • Twitter sentiment analysis.
Module 5: Naïve Bayes Classifier
  • Naïve Bayes for text classification
  • New articles tagging
Module 6: Artificial Neural Network (ANN)
  • Basic ANN network for regression and classification

Module 7: Tensorflow overview and Deep Learning Intro
  • Tensorflow workflow demo and intro to deep learning

Course 9: Sentiment Analysis

Module 1: Introduction to Sentiment Analysis
  • Sentiment Polarity

Module 2: Introduction to NLTK and TextBlob packages
  • Hands-on Sentiment Analysis with NLTK and TextBlob
Module 3: Application of Sentiment Analysis on Twitter live
  • Connecting to Twitter API and Live hands-on sentiment analysis use case

Course 10: Deep Learning Foundation

Module 1: Introduction to Deep Learning
  • What is deep learning? Deep Learning models

Module 2: Deep Learning with Python frameworks
  • Keras
  • Tensorflow
Module 3: Applications of Deep Learning
  • Various applications of Deep Learning

Course 11: Artificial Intelligence Foundation

Module 1: Artificial Intelligence Introduction
  • Core concepts of Artificial Intelligence

Module 2: Domains of Artificial Intelligence
  • Computer Vision, NLP, ML & DL, Robotics

Module 3: Applications of Artificial Intelligence
  • Various industry applications of AI

Module 4: Limitations of Artificial Intelligence
  • Major limitations of AI Adoptions

Course 12: AI Model Deployment

Module 1: AI model deployment strategies
  • Various model deployment strategies

Module 2: Simple API deployment
  • API deployment with FLASK framework

Module 3: Creating website based on API deployed
  • Creating HTML front-end for API

Course 13: Convolutional Neural Network

Module 1: Introduction to CNN
  • Convolution – feature maps, max pooling, ANN

Module 2: Image Processing fundamentals
  • Image Basics, Converting image to Numpy Array

Module 3: Convolution Filter Explanation
  • Various kinds of filters – edge filter

Course 14: CNN Hands-On Project

Module 1: Introduction to Image classification coding
  • Keras with TensorFlow, hands-on image classification CNN
Module 2: Keras code for classifying Cats and Dogs
  • Python Keras coding for image classification

Module 3: Creating a predicting model with TensorFlow as backend.
  • Complete CNN Code

Course 15: Flask – API Model Development

Module 1: REST API
  • API concepts
  • Web servers
  • URL parameters
Module 2: FLASK Web framework
  • FLASK Web framework Installing flask
  • Configuration
Module 3: API in Flask
  • API coding in Flask

Module 4: End to End Deployment
  • Exporting trained model, creating end to end API

Admission details

Step 1: To know about the detailed admission procedure for the Diploma in Data Science online programme, visit the course page first.

Step 2: Upon landing, click on the ‘Enquire Now’ option and enter your name, email, phone number, and company name. DataMites will get in touch with you.

Evaluation process

The Diploma in Data Science classes certification exam is available in both online and paper-based format. You can choose any one option depending on your preferences. An examination fee is also charged, which is included in the course fee. 

How it helps

The Diploma in Data Science certification course offers a host of benefits, some of which are specialised syllabus, career assistance, and expert trainers. During the programme, you also get to work on several client and capstone projects, which allow you to bring your data science skills to a whole new level. You also get access to the DataMites cloud lab for practice. Moreover, this programme perfectly aligns with the industry requirements and provides exposure to all the latest tools and techniques. 

Upon completion, you also receive a certification from IABAC and DMC, which can add significant weight to your credentials and open many new career opportunities.

FAQs

If I want to discontinue the online Diploma in Data Science course, do I get a refund?

Yes, 100% of the fee will be refunded if you don’t find the course up to the mark. However, the examination fee will not be refunded.

Do I get extra study material for further study?

Yes, DataMites will provide you with cheat sheets, data sets, videos, and other study material for your practice.

Can I pay the fee for the Diploma in Data Science online course in instalments?

The Diploma in Data Science course fee needs to be paid in one go for booking your seat with IABAC. If you have financial constraints, you can get in touch with the DataMites relationship manager.

How do I validate the certificate?

You can validate your IABAC certification by entering your unique certification number at the IABAC.org portal.

Do I receive job assistance post-completion?

DataMites placement assistance team (PAT) will work alongside you post-completion to get you the right Data Science job.

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