CERTIFIED DATA SCIENTIST COURSE

Data Science Foundation is a high level Data Science course designed with agenda covering all aspects of data science with core concepts. Data Science has three board domains viz., Statistics, Machine Learning/ Programming/ Data Skills, Business Domain Knowledge. The foundation courses focus on basic statistics, overview of Machine Learning with hands on practice with couple of ML algorithms and application of Data Science in top 5 industry domains. This also includes two hands-on Data Science case studies, enabling the candidates to not only understand the core concepts but also gain practical hands on knowledge, thereby boosting the confidence to pursue further knowledge in the field of Data Science.

CERTIFIED DATA SCIENTIST COURSE Training Cost

Classroom

  • 8-Day(4 weekends) Intensive Program
  • 3 Months Live Project Mentoring
88000
44000

Live Virtual

  • 80 Hrs Live Virtual Intensive Program
  • 3 Months Live Project Mentoring
79000
39000

Self Learning

  • 1 Year Access to Elearning content
  • 3 Months of Live Project Mentoring
44000
22000

CLASSROOM TRAINING SCHEDULES NEAR BY

Data Science Success Stories

Description

DataMites™ Certified Data scientist is designed to provide a right blend of all four facets of Data Science

  • This four facets forms four pillars for data science field. They are 1. Programing 2. Statistics 3. Machine Learning 4. Business Knowledge.
  • The course is mainly focussed on Python for core data science programing, it also includes R as necessary to enable professionals working in R.
  • Statistics are covered as required for a Data Scientist, you may find detailed syllabus in syllabus tab.
  • Machine Learning is the main tool kit for Data Science in predicting classification or regression.
  • This course courses all popular ML algorithms as detailed in syllabus tab.
  • This course allows candidates to obtain an in-depth knowledge by laying a strong foundation and covering all the latest data science topics.
  • The increasing demand curve for data science professionals to manage the large set of data in various organizations providing millions of job opportunities in global markets.
  • The knowledge gained through this course along with IABAC™ certificate surely help you to become data science professional.

This course comes as a perfect package of required Data Science skills including programing , statistics and Machine Learning. If you aspire to be Data Science professionals, this course can immensely help you to reach your goal.

After successful completion of this “Certified Data scientist” course, you should have

  • Gained a better knowledge on entire Data Science project work flow.
  • Understand key concepts of statictics
  • Gained hands on knowledge of popular Machine learning algorithms
  • In depth knowledge on Data Mining, Data forecasting, and Data Visualization.
  • Able to create business case for Data Science project
  • Deliver end to end data science project to customer

Data science is the hottest field in the market as on today. Be it a small company or an MNC, they need a Data scientist to manage their large pool of data.

  • High demand for data scientists with only a few qualified people to hire
  • High salaries, nearly twice of an average software engineer as per Glassdoor report
  • This course not only designed to enable you for new career opportunities but also allows you to apply the new age skills in your current work and become valuable to in your current role.
  • Be assured that you are entering the future of data science much earlier to grab those wonderful opportunities arising from this biggest need of the business world.

This course “Certified Data scientist” is not restricted to any specific domain.

  • Fresh Graduates or students from any discipline can choose this course to obtain better job opportunities in this most demanding data science field
  • Working professionals looking to change their domain to data science field.
  • Highly recommended for those who are aspiring jobs that mainly revolves around data analytics and machine learning
  • Project managers aspiring to switch to manage Data Science projects

DataMites™ is the global institute for Data Science accredited by International Association of Business Analytics Certifications (IABAC). DataMites provides flexible learning options from Classroom training, Live Online to high quality recorded sessions

The 6 Key reasons to choose Data Mites™

IABAC™ Accredited

  • Globally reputed certification
  • Syllabus Aligned with IABAC global market standards

Elite Faculty & Mentors

  • Best in industry faculty from IIMs
  • Course structured by Professors in Data Science from top universities
  • Ensures high quality learning experience

Learning Approach

  • Learning through case study approach
  • Theory → Hands On → Case Study → Project → Model Deployment

10+ Industry Projects

  • 10+ Industry related projects
  • Enabling candidates to gain real time skills, also boosting confidence for real challenges

PAT (Placement Assistance Team)

  • Dedicated PAT (Placement assistanceTeam)
  • Resume assist service
  • Mapping candidates to verified jobs by PAT team
  • Supporting in Interview preparation

24x7 Cloud Lab for ONE year

  • High capacity data science cloud lab
  • All Machine Learning python and R scripts on cloud lab for quick reference
  • Enable participants to practice Data Science even with their mobile phones through cloud lab

Syllabus

The following topics are covered here

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: Visualization 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

The following topics are covered here

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 - Normalization
  • Normality Tests
  • Skewness
  • Kurtosis

Module 5: Hypothesis & computational Techniques

  • Hypothesis Testing
  • Null Hypothesis
  • P-Value
  • Type I & II errors
  • Parametric Testing: T - Tests
  • Anova Test
  • Non-Parametric Testing
  • Ascerting accuracy of Data

Module 6: Correlation & Regression

  • Introduction to Regression
  • Type of Regression
  • Hands on of Regression with R and Python.
  • Correlation
  • Weak and Strong Correlation
  • Finding Correlation with R and Python

The following topics are covered here

Module 1: Machine Learning Introduction

  • What is Machine Learning
  • Machine Learning vs Artificial Intelligence
  • Machine Learning Workflow
  • Statistical Modeling of Machine Learning
  • Application of Machine Learning

Module 2: Machine Learning Algorithms

  • Popular Machine Learning Algorithms
  • Clustering, Classification and Regression
  • Supervised vs Unsupervised Learning
  • Choice of Machine Learning

Module 3: Supervised Learning

  • Simple and Multiple Linear Regression
  • KNN etc...

Module 4: Linear Regression and Logistic Regression

  • Theory of Linear Regression
  • Hands on with use Cases

Module 5: K-Nearest Neighbour (KNN)

Module 6: Decision Tree

Module 7: Naïve Bayes Classifier

Module 8: Unsupervised Learning

  • K-means Clustering

The following topics are covered here

Module 1: Advanced Machine Learning Concepts

  • Tuning with Hyper Parameters
  • Popular ML Algorithms
  • Clustering, Classification and Regression
  • Supervised vs Unsupervised
  • Choice of ML Algorithm

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 8: Tensorflow overview and Deep Learning Intro

  • Tensorflow work flow demo and intro to deep learning

Module 1: What is Time Series?

Module 2: Trend, Seasonality, cyclical and random

Module 3: White Noise

Module 4: Auto Regressive Model (AR)

Module 5: Moving Average Model (MA)

Module 6: ARMA Model

Module 7: Stationarity of Time Series

Module 8: ARIMA Model – Prediction Concepts

Module 9: ARIMA Model Hands on with Python

Module 10: Case Study Assignment on ARIMA

Module 1: REST API

  • API Concepts
  • Web Servers.
  • URL parameters

Module 2: 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

Module 1: Image Processing fundamentals

  • Image Basics
  • Converting Image to Numpy Array

Module 2: Introduction to CNN

  • Convolution – Feature Maps
  • Max Pooling
  • ANN

Module 3: Image Classification (Cats and Dogs)

  • Keras with Tensorflow
  • Hands on Image Classification CNN

Module 1: Understanding Business Case

  • Components of Business Case
  • ROI Calculation Techniques
  • Scoping

Module 2: Writing Data Science Business Case

  • Defining Business Opportunity
  • Translating to Data Science problem
  • Creating project plan

Module 3: Benefits Analysis

  • Demonstrating Break Even and Benefits Analysis with Data Science Solutions

Module 4: Starting project, Setting up Team and closing

  • Initiating Project
  • Setting up the Team
  • Delivering and Closing Project

Module 1: Tableau Introduction

  • Tableau Interface
  • Dimensions and Measures
  • Filter Shelf
  • Distributing and Publishing

Module 2: Connecting to Data Source

  • Connecting to Sources
  • Excel
  • Data Bases
  • Api
  • PDF
  • Extracting and interpreting data

Module 3: Visual Analytics

  • Charts and Plots with Super Store Data

Module 4: Forecasting

  • Forecasting Time Series Data
  • Sales

FAQ'S

Total course fee should be paid before 50% of the course completion. We also have EMI option tied up with bank. Check with coordinators.

No, Most of software are free and open source. The guidelines to setup software is a part of course.

Certified Data Scientist is delivered in both Classroom and Online mode. Classroom is provided in selected cities in India such as Bangalore, Hyderabad.

Yes. The IABAC Exam fee is included in the course fee. No extra fee is charged.

All the online sessions are recorded and shared so you can revise the missed session. For Classroom, speak to the coordinator to join the session in another batch.

We have a dedicated PAT (Placement Assistance team) to provide 100% support in finding your dream job.

Data Science Interview Questions

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