DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE COURSE LEAD MENTORS

DATA SCIENCE COURSE FEE IN RAIPUR

Live Virtual

Instructor Led Live Online

110,000
72,345

  • IABAC® & NASSCOM® Certification
  • 8-Month | 700 Learning Hours
  • 120-Hour Live Online Training
  • 25 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

66,000
43,995

  • Self Learning + Live Mentoring
  • IABAC® & NASSCOM® Certification
  • 1 Year Access To Elearning
  • 25 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Leaner assistance and support

Classroom

In - Person Classroom Training

110,000
82,845

  • IABAC® & NASSCOM® Certification
  • 8-Month | 700 Learning Hours
  • 120-Hour Classroom Sessions
  • 25 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship + Job Assistance

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

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UPCOMING DATA SCIENCE ONLINE CLASSES IN RAIPUR

BEST DATA SCIENCE CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES INSTITUTE FOR DATA SCIENCE COURSE

Why DataMites Infographic

SYLLABUS OF DATA SCIENCE COURSE IN RAIPUR

MODULE 1: DATA SCIENCE COURSE INTRODUCTION 

  • CDS Course Introduction
  • 3 Phase Learning
  • Learning Resources
  • Assessments & Certification Exams
  • DataMites Mobile App
  • Support Channels

MODULE 2: DATA SCIENCE ESSENTIALS 

  • Introduction to Data Science
  • Evolution of Data Science
  • Data Science Terminologies
  • Data Science vs AI/Machine Learning
  • Data Science vs Analytics

MODULE 3: DATA SCIENCE DEMO 

  • Business Requirement: Use Case
  • Data Preparation
  • Machine learning Model building
  • Prediction with ML model
  • Delivering Business Value

MODULE 4: ANALYTICS CLASSIFICATION 

  • Types of Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics

MODULE 5: DATA SCIENCE AND RELATED FIELDS

  • Introduction to AI
  • Introduction to Computer Vision
  • Introduction to Natural Language Processing
  • Introduction to Reinforcement Learning
  • Introduction to GAN
  • Introduction to  Generative Passive Models

MODULE 6: DATA SCIENCE ROLES & WORKFLOW

  • Data Science Project workflow
  • Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
  • Data Science Project stages

MODULE 7: MACHINE LEARNING INTRODUCTION

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 8: DATA SCIENCE INDUSTRY APPLICATIONS 

  • Data Science in Finance and Banking
  • Data Science in Retail
  • Data Science in Health Care
  • Data Science in Logistics and Supply Chain
  • Data Science in Technology Industry
  • Data Science in Manufacturing
  • Data Science in Agriculture

MODULE 1: PYTHON BASICS 

  • Introduction of python
  • Installation of Python and IDE
  • Python objects
  • Python basic data types
  • Number & Booleans, strings
  • Arithmetic Operators
  • Comparison Operators
  • Assignment Operators
  • Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS 

  • IF Conditional statement
  • IF-ELSE • NESTED IF
  • Python Loops basics
  • WHILE Statement
  • FOR statements
  • BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES 

  • Basic data structure in python
  • String object basics and inbuilt methods
  • List: Object, methods, comprehensions
  • Tuple: Object, methods, comprehensions
  • Sets: Object, methods, comprehensions
  • Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS 

  • Functions basics
  • Function Parameter passing
  • Iterators
  • Generator functions
  • Lambda functions
  • Map, reduce, filter functions

MODULE 5: PYTHON NUMPY PACKAGE 

  • NumPy Introduction
  • Array – Data Structure
  • Core Numpy functions
  • Matrix Operations

MODULE 6: PYTHON PANDASPACKAGE

  • Pandasfunctions
  • Data Frame and Series – Data Structure
  • Data munging with Pandas
  • Imputation and outlier analysis

 

MODULE 1: OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2: HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3: EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4: HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5: CORRELATION AND REGRESSION 

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method

 

MODULE 1: MACHINE LEARNING INTRODUCTION 

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 2: PYTHON NUMPY & PANDAS PACKAGE 

  • NumPy & Pandas functions
  • Array – Data Structure
  • Core Numpy functions
  • Matrix Operations
  • Data Frame and Series – Data Structure
  • Data munging with Pandas
  • Imputation and outlier analysis

MODULE 3: VISUALIZATION WITH PYTHON 

  • Visualization Packages (Matplotlib)
  • Components Of A Plot, Sub-Plots
  • Basic Plots: Line, Bar, Pie, Scatter
  • Advanced Python Data Visualizations

MODULE 4: ML ALGO: LINEAR REGRESSION

  • Introduction to Linear Regression
  • How it works: Regression and Best Fit Line
  • Modeling and Evaluation in Python

MODULE 5: ML ALGO: KNN 

  • Introduction to KNN
  • How It Works: Nearest Neighbor Concept
  • Modeling and Evaluation in Python

MODULE 6: ML ALGO: LOGISTIC REGRESSION 

  • Introduction to Logistic Regression
  • How it works: Classification & Sigmoid Curve
  • Modeling and Evaluation in Python

MODULE 7: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA in Python

MODULE 8: ML ALGO: K MEANS CLUSTERING 

  • Understanding Clustering (Unsupervised)
  • K Means Algorithm
  • How it works: K Means theory
  • Modeling in Python

MODULE 1: MACHINE LEARNING INTRODUCTION 

  • What Is ML? ML Vs AI
  • ML Workflow, Popular ML Algorithms
  • Clustering, Classification And Regression
  • Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSSION 

  • Introduction to Linear Regression
  • How it works: Regression and Best Fit Line
  • Modeling and Evaluation in Python

MODULE 3: ML ALGO: LOGISTIC REGRESSION 

  • Introduction to Logistic Regression
  • How it works: Classification & Sigmoid Curve
  • Modeling and Evaluation in Python

MODULE 4: ML ALGO: KNN 

  • Introduction to KNN
  • How It Works: Nearest Neighbor Concept
  • Modeling and Evaluation in Python

MODULE 5: ML ALGO: K MEANS CLUSTERING 

  • Understanding Clustering (Unsupervised)
  • K Means Algorithm
  • How it works : K Means theory
  • Modeling in Python

MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA) 

  • Building Blocks Of PCA
  • How it works: Finding Principal Components
  • Modeling PCA in Python

MODULE 7: ML ALGO: DECISION TREE 

  • Random Forest Ensemble technique
  • How it works: Bagging Theory
  • Modeling and Evaluation in Python

MODULE 8 : ML ALGO: NAÏVE BAYES 

  • Introduction to Naive Bayes
  • How it works: Bayes' Theorem
  • Naive Bayes For Text Classification
  • Modeling and Evaluation in Python

MODULE 9: GRADIENT BOOSTING, XGBOOST 

  • Introduction to Boosting and XGBoost
  • How it works: weak learners' concept
  • Modeling and Evaluation of in Python

MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE  (SVM) 

  • Introduction to SVM
  • How It Works: SVM Concept, Kernel Trick
  • Modeling and Evaluation of SVM in Python

MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN) 

  • Introduction to ANN
  • How It Works: Back prop, Gradient Descent
  • Modeling and Evaluation of ANN in Python

MODULE 12: ADVANCED ML CONCEPTS 

  • Adv Metrics (Roc_Auc, R2, Precision, Recall)
  • K-Fold Cross-validation
  • Grid And Randomized Search CV In Sklearn
  • Imbalanced Data Set: Smote Technique
  • Feature Selection Techniques

MODULE 1: TIME SERIES FORECASTING - ARIMA 

  • What is Time Series?
  • Trend, Seasonality, cyclical and random
  • Autoregressive Model (AR)
  • Moving Average Model (MA)
  • Stationarity of Time Series
  • ARIMA Model
  • Autocorrelation and AIC 

MODULE 2: FEATURE ENGINEERING 

  • Introduction to Features Engineering
  • Transforming Predictors
  • Feature Selection methods
  • Backward elimination technique
  • Feature importance from ML modeling

MODULE 3: SENTIMENT ANALYSIS 

  • Introduction to Sentiment Analysis
  • Python packages: TextBlob, NLTK
  • Case study: Twitter Live Sentiment Analysis

MODULE 4: REGULAR EXPRESSIONS WITH PYTHON 

  • Regex Introduction
  • Regex codes
  • Text extraction with Python Regex

MODULE 5: ML MODEL DEPLOYMENT WITH FLASK

  • Introduction to Flask
  • URL and App routing
  • Flask application – ML Model deployment

MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL 

  • MS Excel core Functions
  • Pivot Table
  • Advanced Functions (VLOOKUP, INDIRECT..)
  • Linear Regression with EXCEL
  • Goal Seek Analysis
  • Data Table
  • Solving Data Equation with EXCEL
  • Monte Carlo Simulation with MS EXCEL

MODULE 7: AWS CLOUD FOR DATA SCIENCE

  • Introduction of cloud
  • Difference between GCC, Azure,AWS
  • AWS Service ( EC2 and S3 service)
  • AWS Service (AMI), AWS Service (RDS)
  • AWS Service (IAM), AWS (Athena service)
  • AWS (EMR), AWS, AWS (Redshift)
  • ML Modeling with AWS Sage Maker 

MODULE 8: AZURE FOR DATA SCIENCE 

  • Introduction to AZURE ML studio
  • Data Pipeline and ML modeling with Azure

MODULE 1: DATABASE INTRODUCTION 

  • DATABASE Overview
  • Key concepts of database management
  • CRUD Operations
  • Relational Database Management System
  • RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS 

  • Introduction to Databases
  • Introduction to SQL
  • SQL Commands
  • MY SQL  workbench installation
  • Comments
  • import and export dataset

MODULE 3: DATA TYPES AND CONSTRAINTS 

  • Numeric, Character, date time data type
  • Primary key, Foreign key, Not null
  • Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL) 

  • Create database
  • Delete database
  • Show and use databases
  • Create table, Rename table
  • Delete table, Delete  table records
  • Create new table from existing data types
  • Insert into, Update records
  • Alter table

MODULE 5: SQL JOINS 

  • Inner join
  • Outer join
  • Left join
  • Right join
  • Cross join
  • Self join

MODULE 6: SQL COMMANDS AND CLAUSES 

  • Select, Select distinct
  • Aliases, Where clause
  • Relational operators, Logical
  • Between, Order by, In
  • Like, Limit, null/not null, group by
  • Having, Sub queries

MODULE 7 : DOCUMENT DB/NO-SQL DB 

  • Introduction of Document DB
  • Document DB vs SQL DB
  • Popular Document DBs
  • MongoDB basics
  • Data format and Key methods
  • MongoDB data management

MODULE 1: GIT  INTRODUCTION 

  • Purpose of Version Control
  • Popular Version control tools
  • Git Distribution Version Control
  • Terminologies
  • Git Workflow
  • Git Architecture

MODULE 2: GIT REPOSITORY and GitHub 

  • Git Repo Introduction
  • Create New Repo with Init command
  • Copying existing repo
  • Git user and remote node
  • Git Status and rebase
  • Review Repo History
  • GitHub Cloud Remote Repo

MODULE 3: COMMITS, PULL, FETCH AND PUSH 

  • Code commits
  • Pull, Fetch and conflicts resolution
  • Pushing to Remote Repo

MODULE 4: TAGGING, BRANCHING AND MERGING 

  • Organize code with branches
  • Checkout branch
  • Merge branches

MODULE 5: UNDOING CHANGES 

  • Editing Commits
  • Commit command Amend flag
  • Git reset and revert

MODULE 6: GIT WITH GITHUB AND BITBUCKET 

  • Creating GitHub Account
  • Local and Remote Repo
  • Collaborating with other developers
  • Bitbucket Git account

MODULE 1: BIG DATA INTRODUCTION 

  • Big Data Overview
  • Five Vs of Big Data
  • What is Big Data and Hadoop
  • Introduction to Hadoop
  • Components of Hadoop Ecosystem
  • Big Data Analytics Introduction

MODULE 2 : HDFS AND MAP REDUCE 

  • HDFS – Big Data Storage
  • Distributed Processing with Map Reduce
  • Mapping and reducing  stages concepts
  • Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
  • Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION 

  • PySpark Introduction
  • Spark Configuration
  • Resilient distributed datasets (RDD)
  • Working with RDDs in PySpark
  • Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE 

  • Introducing Spark SQL
  • Spark SQL vs Hadoop Hive
  • Working with Spark SQL Query Language

MODULE 5 : MACHINE LEARNING WITH SPARK ML 

  • Introduction to MLlib Various ML algorithms supported by MLib
  • ML model with Spark ML
  • Linear regression
  • logistic regression
  • Random forest

MODULE 6: KAFKA and Spark 

  • Kafka architecture
  • Kafka workflow
  • Configuring Kafka cluster
  • Operations

MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION 

  • What Is Business Intelligence (BI)?
  • What Bi Is The Core Of Business Decisions?
  • BI Evolution
  • Business Intelligence Vs Business Analytics
  • Data Driven Decisions With Bi Tools
  • The Crisp-Dm Methodology

MODULE 2: BI WITH TABLEAU: INTRODUCTION

  • The Tableau Interface
  • Tableau Workbook, Sheets And Dashboards
  • Filter Shelf, Rows And Columns
  • Dimensions And Measures
  • Distributing And Publishing

MODULE 3 : TABLEAU: CONNECTING TO DATA SOURCE 

  • Connecting To Data File , Database Servers
  • Managing Fields
  • Managing Extracts
  • Saving And Publishing Data Sources
  • Data Prep With Text And Excel Files
  • Join Types With Union
  • Cross-Database Joins
  • Data Blending
  • Connecting To Pdfs

MODULE 4: TABLEAU : BUSINESS INSIGHTS 

  • Getting Started With Visual Analytics
  • Drill Down And Hierarchies
  • Sorting & Grouping
  • Creating And Working Sets
  • Using The Filter Shelf
  • Interactive Filters
  • Parameters
  • The Formatting Pane
  • Trend Lines & Reference Lines
  • Forecasting
  • Clustering

MODULE 5: DASHBOARDS, STORIES AND PAGES 

  • Dashboards And Stories Introduction
  • Building A Dashboard
  • Dashboard Objects
  • Dashboard Formatting
  • Dashboard Interactivity Using Actions
  • Story Points
  • Animation With Pages

MODULE 6: BI WITH POWER-BI 

  • Power BI basics
  • Basics Visualizations
  • Business Insights with Power BI

OFFERED DATA SCIENCE COURSES IN RAIPUR

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN RAIPUR

Data science has emerged as a game-changer across industries, enabling organizations to make data-driven decisions, gain valuable insights, and unlock new opportunities. Data science professionals in Raipur are in high demand, which will empower you to thrive in the data-driven era. According to a Polaris Market Research report, the market size of the data science platforms is expected to reach USD 695.0 Billion and grow at a CAGR rate of 27.6%. 

DataMites, a globally renowned institute, offers comprehensive Data Science courses in Raipur, encompassing recognized disciplines such as artificial intelligence, machine learning, data analytics, and deep learning. The institute provides flexible on-demand data science offline classes in Raipur, allowing students to learn at their convenience. The course spans over 8 months and includes 700 hours of learning, with an additional 120 hours of live online training. DataMites is known for its IABAC-certified courses, which have proven beneficial for learners worldwide. Additionally, DataMites assists students with internship and job placements, providing them with valuable career opportunities. Aspiring professionals can take advantage of the exclusive Certified Data Scientist Course in Raipur to enhance their skills and prospects.

DataMites provides key features for Data Science Training in Raipur that include:

  1. Faculty and Ashok Veda as Lead Mentor
  2. Course Curriculum
  3. Resume Preparation
  4. Live client project
  5. Flexible Training Modes
  6. Hands-on Projects
  7. Global Certification
  8. 24-hour job and placement assistance 
  9. Intensive live online training

Raipur, the capital city of Chhattisgarh, blends rich cultural heritage with modern development, offering a vibrant and dynamic environment for residents and visitors alike. The future scope of data science in Raipur is promising, with opportunities for professionals to leverage data-driven insights for decision-making and innovation, driving growth and efficiency across industries in the region. The salary of a data scientist in India ranges from INR 11,30,556 per year according to a Glassdoor report. At DataMites, the students get data science certification in Raipur after the completion of the training program. DataMites offers online data science training in Raipur with a comprehensive syllabus, study material, job training, and mock tests. Join the DataMites Data Science Training Institute in Raipur towards becoming a certified data scientist and unlock its realm of promising future.

Along with the data science courses, DataMites also provides artificial intelligence,  machine learning, deep learning, python, mlops, AI expert, tableau, IoT, data analyst, data engineer training, r programming and data analytics courses in Raipur.

ABOUT DATAMITES DATA SCIENCE COURSE IN RAIPUR

Data science is an interdisciplinary field that involves extracting insights and knowledge from data through various techniques such as statistics, machine learning, and data visualization. It combines elements of mathematics, statistics, computer science, and domain expertise to analyze and interpret complex data sets, ultimately enabling data-driven decision-making.

Learning data science is crucial because it equips individuals with the skills and knowledge to extract valuable insights from vast amounts of data. With data being generated at an unprecedented rate, organizations across industries are increasingly relying on data science to make informed business decisions, optimize processes, and gain a competitive edge.

To become a data scientist, you need a combination of technical and soft skills. Technical skills include proficiency in programming languages (such as Python or R), statistical analysis, machine learning, data visualization, and database querying. Soft skills encompass critical thinking, problem-solving, communication, and domain knowledge in the area you wish to apply data science.

To learn data science effectively, it is recommended to follow a structured approach. Start with a strong foundation in mathematics and statistics, then learn programming languages commonly used in data science, such as Python or R. Gain hands-on experience by working on real-world projects, participate in online courses or bootcamps, and explore relevant books, tutorials, and resources. Continuous practice, staying updated with industry trends, and joining data science communities can also enhance your learning journey.

Data scientists often face challenges such as accessing and cleaning data, dealing with missing or inconsistent data, managing large and complex datasets, selecting appropriate algorithms for analysis, and interpreting the results accurately. They may also encounter challenges related to communication with non-technical stakeholders and keeping up with the rapid advancements in the field.

The cost of a data science course in Raipur ranges from INR 40,000 to INR 50,000 depending on the institute, course duration, and curriculum.

The eligibility criteria for learning a data science course can vary depending on the institute or program. Generally, a strong foundation in mathematics and statistics is beneficial. Many data science courses are open to individuals with a background in computer science, engineering, or a related field. However, some courses may also accept candidates from diverse backgrounds who demonstrate an aptitude for data science.

The scope of data science is vast and expanding across various industries. With the increasing availability of data and the need to extract meaningful insights, data scientists are in high demand. They can find opportunities in sectors such as finance, healthcare, e-commerce, marketing, telecommunications, and many more. The scope includes roles such as data analysts, data scientists, machine learning engineers, and data engineers.

A data science certification can provide several benefits. It validates your knowledge and skills in data science, making you more competitive in the job market. It demonstrates your commitment to continuous learning and professional development. Certifications can also help you gain credibility with employers and increase your chances of securing data science-related roles or advancing in your career.

Yes, there is a significant demand for data science courses. As the importance of data-driven decision-making grows across industries, there is an increasing need for professionals with data science skills. 

Yes, SQL (Structured Query Language) is an important skill for data scientists. It is commonly used to retrieve, manipulate, and analyze data stored in relational databases. SQL allows data scientists to extract relevant information, perform data transformations, and create new tables or views for analysis. Proficiency in SQL can greatly enhance a data scientist's ability to work with data efficiently.

The career outlook for a fresher in data science is promising. With the increasing demand for data-driven decision-making, there is a need for skilled data scientists. As a fresher, you can start your career as a data analyst, junior data scientist, or data engineer. With time and experience, you can progress to more senior roles and take on responsibilities such as developing machine learning models, leading data science projects, and making strategic data-driven decisions.

Several top companies across industries are actively hiring data science freshers. Some notable examples include technology giants like Google, Microsoft, Amazon, Facebook, and Apple. Additionally, consulting firms like Deloitte, Accenture, and McKinsey, as well as financial institutions, healthcare organizations, e-commerce companies, and startups, are also hiring data science talent.

Yes, statistics is a fundamental component of data science. Understanding statistical concepts and techniques is crucial for analyzing and interpreting data accurately. Data scientists use statistical methods to summarize and describe data, identify patterns and trends, test hypotheses, and make predictions. Proficiency in statistics enables data scientists to draw meaningful insights from data and make informed decisions.

DataMites offers a range of data science courses, including:

  • Data Science Foundation: An introductory course covering the basics of data science, including statistics, programming, and machine learning.
  • Certified Data Scientist: A comprehensive program covering various aspects of data science, including data preprocessing, exploratory data analysis, statistical modeling, machine learning algorithms, and data visualization.
  • Certified AI Engineer: A course focused on artificial intelligence (AI) and machine learning (ML) techniques, including deep learning, natural language processing, computer vision, and neural networks.
  • Big Data Engineering: A course that covers big data technologies like Hadoop, Spark, and NoSQL databases, along with data processing and storage techniques.

CDS can refer to multiple things in different contexts. In the context of data science, CDS stands for "Certified Data Scientist." It may refer to a certification offered by a particular organization or institute, validating the skills and knowledge of an individual in the field of data science. However, without specific information, it is challenging to provide a more precise answer.

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FAQ’S OF DATA SCIENCE TRAINING IN RAIPUR

The Data Science course offered by DataMites in Raipur is a comprehensive program that encompasses essential topics, hands-on experience through real-world projects, and industry-recognized certifications. It is an excellent option for individuals who are looking to succeed and thrive in the field of Data Science.

The Certified Data Scientist Course provided by DataMites in Raipur welcomes individuals who possess a solid background in mathematics and programming, as well as those with prior experience in statistics, engineering, or related disciplines. This inclusivity makes the program ideal for a diverse range of participants who aspire to build a successful career in the field of Data Science.

Opting for the data science course offered by DataMites in Raipur can be advantageous due to its all-encompassing training, hands-on experience gained from real-world projects, and industry-acknowledged certifications. This course equips individuals with the essential skills and knowledge required to thrive in the data science industry.

The course spans over 8 months and includes 700 hours of learning, with an additional 120 hours of live online training.

Yes, after completion of the data science course in Raipur, the students are certified with globally recognized IABAC certification which helps them during job and internship programs.

Upon course completion, DataMites provides dedicated support and guidance for placements through their Placement Assistance Team (PAT). This ensures that individuals receive comprehensive assistance in securing employment opportunities, enhancing their chances of finding suitable job placements.

DataMites offers a diverse range of data science courses in Raipur, including Data Science Foundation, Data Science for Managers, Data Science Associate, Diploma in Data Science, Python for Data Science, Statistics for Data Science, Data Science Marketing, Data Science Operations, Data Science Retail, Data Science for HR, Data Science with Finance, and Data Science.

DataMites is renowned for its team of highly experienced educators in the field of data science. These instructors possess extensive expertise, along with the necessary qualifications and certifications. With their wealth of experience, they provide exceptional instruction, enabling students to gain a comprehensive understanding of the subject matter.

DataMites offers flexible learning options to cater to the preferences of students. They provide a variety of choices, including live online sessions, self-paced learning methods, and on-demand classroom training. This flexibility allows individuals to select the learning approach that best suits their needs and enables them to conveniently pursue their data science education.

DataMites offers an overview of its training approach and provides a complimentary demo class, allowing students to enhance their understanding of the training process and its components.

Learning Through Case Study Approach

Theory → Hands-on → Case Study → Project → Model Deployment

The payment mode available for the data science course in Raipur through:

  • Cash
  • Net Banking
  • Check
  • Debit Card
  • Credit Card
  • PayPal
  • Visa
  • Master card
  • American Express

DataMites Data Science Course in Raipur is available at different price points: INR 35,000 for live online training, INR 21,000 for blended learning, and INR 44,000 for on-demand classroom training.

Yes, To issue the participation certificate and book the certification exam, it is necessary to provide photo identification proofs such as a National ID card or a Driving license.

The salary of a data scientist in India ranges from INR 11,30,556 per year according to a Glassdoor report.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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