DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE COURSE LEAD MENTORS

DATA SCIENCE COURSE FEE IN BANER, PUNE

Live Virtual

Instructor Led Live Online

110,000
59,451

  • 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
34,951

  • 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
64,451

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

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UPCOMING DATA SCIENCE ONLINE CLASSES IN BANER

UPCOMING DATA SCIENCE OFFLINE CLASSES IN BANER

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 FOR DATA SCIENCE TRAINING

Why DataMites Infographic

SYLLABUS OF DATA SCIENCE CERTIFICATION COURSE

MODULE 1: DATA SCIENCE ESSENTIALS 

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

MODULE 2: DATA SCIENCE DEMO

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

MODULE 3: ANALYTICS CLASSIFICATION 

 • Types of Analytics
 • Descriptive Analytics
 • Diagnostic Analytics
 • Predictive Analytics
 • Prescriptive Analytics
 • EDA and insight gathering demo in Tableau

MODULE 4: 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 5: DATA SCIENCE ROLES & WORKFLOW

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

MODULE 6: MACHINE LEARNING INTRODUCTION

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

MODULE 7: 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 Variables
 • Python basic data types
 • Number & Booleans, strings
 • Arithmetic Operators
 • Comparison Operators
 • Assignment Operators

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
 • Basics of List
 • List: Object, methods
 • Tuple: Object, methods
 • Sets: Object, methods
 • Dictionary: Object, methods

MODULE 4: PYTHON FUNCTIONS 

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

MODULE 1: OVERVIEW OF STATISTICS 

 • Introduction to 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
 • Types of Sampling
 • Simple Random Sampling
 • Stratified Random Sampling
 • Cluster Random Sampling
 • Systematic Random Sampling
 • Multi stage Sampling
 • 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 & Properties
 • Z Value / Standard Value
 • Empirical Rule and Outliers
 • Central Limit Theorem
 • Normality Testing
 • Skewness & Kurtosis
 • Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
 • Covariance & Correlation

MODULE 4: HYPOTHESIS TESTING 

 • Hypothesis Testing Introduction
 • P- Value, Critical Region
 • Types of Hypothesis Testing
 • Hypothesis Testing Errors : Type I And Type II
 • Two Sample Independent T-test
 • Two Sample Relation T-test
 • One Way Anova Test
 • Application of Hypothesis testing

 

MODULE 1: MACHINE LEARNING INTRODUCTION 

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

MODULE 2:  PYTHON NUMPY  PACKAGE 

 • Introduction to Numpy Package
 • Array as Data Structure
 • Core Numpy functions
 • Matrix Operations, Broadcasting in Arrays

MODULE 3:  PYTHON PANDAS PACKAGE 

 • Introduction to Pandas package
 • Series in Pandas
 • Data Frame in Pandas
 • File Reading in Pandas
 • Data munging with Pandas

MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib

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

MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN

 • Seaborn: Basic Plot
 • Advanced Python Data Visualizations

MODULE 6: ML ALGO: LINEAR REGRESSSION

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

MODULE 7: ML ALGO: LOGISTIC REGRESSION

 • Introduction to Logistic Regression
 • How it works: Classification & Sigmoid Curve
 • Modeling and Evaluation 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 9: ML ALGO: KNN

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

MODULE 1: FEATURE ENGINEERING 

 • Introduction to Feature Engineering
 • Feature Engineering Techniques: Encoding, Scaling, Data Transformation
 • Handling Missing values, handling outliers
 • Creation of Pipeline
 • Use case for feature engineering

MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

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

MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

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

MODULE 4:  ML ALGO: DECISION TREE 

 • Introduction to Decision Tree & Random Forest
 • How it works
 • Modeling and Evaluation in Python

MODULE 5: ENSEMBLE TECHNIQUES - BAGGING 

 • Introduction to Ensemble technique 
 • Bagging and How it works
 • Modeling and Evaluation in Python

MODULE 6: ML ALGO: NAÏVE BAYES

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

MODULE 7: GRADIENT BOOSTING, XGBOOST

 • Introduction to Boosting and XGBoost
 • How it works?
 • Modeling and Evaluation of in Python

MODULE 1: TIME SERIES FORECASTING - ARIMA 

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

MODULE 2: SENTIMENT ANALYSIS 

 • Introduction to Sentiment Analysis
 • NLTK Package
 • Case study: Sentiment Analysis on Movie Reviews

MODULE 3: REGULAR EXPRESSIONS WITH PYTHON 

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

MODULE 4:  ML MODEL DEPLOYMENT WITH FLASK 

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

MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL

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

MODULE 6:  AWS CLOUD FOR DATA SCIENCE

 • Introduction of cloud
 • Difference between GCC, Azure, AWS
 • AWS Service ( EC2 instance)

MODULE 7: AZURE FOR DATA SCIENCE

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

MODULE 8:  INTRODUCTION TO DEEP LEARNING

 • Introduction to Artificial Neural Network, Architecture
 • Artificial Neural Network in Python
 • Introduction to Convolutional Neural Network, Architecture
 • Convolutional Neural Network in Python

MODULE 1: DATABASE INTRODUCTION 

 • DATABASE Overview
 • Key concepts of database management
 • Relational Database Management System
 • CRUD operations

MODULE 2:  SQL BASICS

 • Introduction to Databases
 • Introduction to SQL
 • SQL Commands
 • MY SQL workbench installation

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
 • Self Join, Cross join
 • Windows function: Over, Partition, Rank

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

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
 • Git Essentials: Copy & User Setup
 • Mastering Git and GitHub

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

MODULE 5: GIT WITH GITHUB AND BITBUCKET

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

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

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

MODULE 1: TABLEAU FUNDAMENTALS 

 • Introduction to Business Intelligence & Introduction to Tableau
 • Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
 • Bar chart, Tree Map, Line Chart
 • Area chart, Combination Charts, Map
 • Dashboards creation, Quick Filters
 • Create Table Calculations
 • Create Calculated Fields
 • Create Custom Hierarchies

MODULE 2:  POWER-BI BASICS

 • Power BI Introduction 
 • Basics Visualizations
 • Dashboard Creation
 • Basic Data Cleaning
 • Basic DAX FUNCTION

MODULE 3 : DATA TRANSFORMATION TECHNIQUES 

 • Exploring Query Editor
 • Data Cleansing and Manipulation:
 • Creating Our Initial Project File
 • Connecting to Our Data Source
 • Editing Rows
 • Changing Data Types
 • Replacing Values

MODULE 4: CONNECTING TO VARIOUS DATA SOURCES 

• Connecting to a CSV File
 • Connecting to a Webpage
 • Extracting Characters
 • Splitting and Merging Columns
 • Creating Conditional Columns
 • Creating Columns from Examples
 • Create Data Model

OFFERED DATA SCIENCE COURSES IN BANER

DATA SCIENCE TRAINING COURSE REVIEWS

ABOUT DATA SCIENCE COURSE IN BANER

In 2019, job postings for data scientists on Indeed skyrocketed by 256%, and according to the U.S. Bureau of Labor Statistics, data science is projected to witness more growth than almost any other field by 2029. This wealth of data emphasizes the pressing need for professionals equipped with the right skills to analyze and derive insights from this information. Addressing this need, DataMites offers comprehensive data science courses in Baner, meticulously designed to empower students for success in this rapidly expanding field.

As an IABAC-accredited institute, DataMites holds a global reputation for delivering high-quality data science education. Our expert faculty, comprised of leading professionals and academics, integrates real-world experience into the classroom setting. We provide comprehensive training covering all aspects of data science, from fundamentals to advanced techniques, ensuring that our students acquire the skills necessary for success in this rapidly growing field. Through our data science training in Baner, participants gain hands-on experience with real-world datasets and master the use of tools such as Python, R, SQL, and more.

DataMites recognizes the diverse preferences of students and offers a range of courses to cater to individual needs. Our data science training offline in Baner provides a traditional classroom setting for personalized interaction with instructors. Alternatively, our online data science training in Baner offers flexibility and convenience without compromising instructional quality. 

For those seeking practical application of theoretical knowledge, our data science training with internships in Baner provide valuable work experience. Additionally, individuals aspiring to advance their careers can explore our data science courses with placement in Baner, offering opportunities with top companies in the field.

For those looking to elevate their careers in data science, a DataMites data science certification in Baner is the ideal pathway. Our courses are meticulously designed to impart the knowledge and skills essential for success in this dynamic field. Notably, Glassdoor reports an average salary of INR 9,52,501 per year for Data Scientists in Pune. Seize the opportunity to join DataMites, the leading global institute for data science courses, and unlock your potential in this exciting and rapidly evolving field.

ABOUT DATAMITES DATA SCIENCE COURSE IN BANER

Data Science encompasses applying scientific methods and algorithms to draw meaningful insights from data. This interdisciplinary field fuses programming, statistical expertise, and subject matter understanding to analyze intricate datasets, discern patterns, and make predictions. It plays a pivotal role in converting raw data into actionable insights, crucial for informed decision-making across various industries.

In our data-centric world, extracting valuable insights is paramount for informed decision-making. Data Science equips us with tools and techniques to analyze, interpret, and visualize data, providing a competitive edge through data-driven decision-making. This not only enhances efficiency and reduces costs but also fosters innovation across diverse industries.

Data Science Certification Courses in Baner are open to individuals seeking to learn, including newcomers and professionals aiming to upskill. While a basic understanding of high school-level subjects is beneficial, these courses cater to diverse backgrounds like engineering, marketing, software development, and IT.

The average data science course fee in Baner ranges from INR 40,000 to INR 1,00,000. This cost variation depends on factors such as the course provider, training level, and duration. Investing in high-quality training is essential for gaining in-depth knowledge and skills in the dynamic field of Data Science.

After completing Data Science Training in Baner, individuals can pursue diverse career opportunities in data analysis, data mining, business intelligence, and machine learning. Job roles like data scientist, data analyst, business analyst, data engineer, and machine learning engineer are in demand across industries such as healthcare, finance, e-commerce, and marketing.

To excel in Data Science Training in Baner, a robust foundation in mathematics and statistics is vital. Proficiency in programming languages like Python and R, familiarity with data visualization and analysis tools like Tableau and Power BI, and possessing critical thinking, problem-solving, and communication skills are highly valued. These skills form the basis for effective data analysis and interpretation.

Learning Data Science may pose challenges such as understanding complex mathematical concepts, coding in languages like Python or R, handling large datasets, and staying updated with evolving technology. Overcoming these hurdles requires a structured learning approach, consistent practice, and perseverance to master the multifaceted aspects of Data Science.

Certainly, recent graduates can enroll in a data science course in Baner and secure entry-level positions post-completion. Many companies actively seek fresh talent with the right skills for roles like data analyst or data scientist. The practical skills gained during the course, coupled with a solid educational background, make recent graduates valuable assets in the data science job market.

Recent graduates completing a data science course in Baner can anticipate numerous job opportunities in the expanding field of data science. Companies actively seek fresh talent capable of deciphering large datasets, extracting valuable insights, and contributing to data-driven decision-making processes.

According to Glassdoor, the average salary for a Data Scientist in Pune is INR ₹9,52,501 per annum. This competitive salary underscores the high demand for skilled professionals in the field of Data Science, highlighting the value placed on individuals proficient in analyzing and interpreting data to drive strategic business decisions.

FAQ'S OF DATA SCIENCE TRAINING IN BANER

DataMites excels in delivering top-notch, industry-relevant Data Science courses. Their approach includes hands-on training, practical experience, extensive course content, industry-recognized certifications, and post-course support. This ensures that students not only acquire theoretical knowledge but also gain practical skills applicable to real-world scenarios.

The duration of DataMites' data science course in Baner ranges from 1 to 8 months. This flexibility accommodates diverse learning needs and schedules, offering both weekday and weekend training sessions to cater to students' preferences.

While several institutes offer Data Science training in Baner, choosing a reputable one is crucial. DataMites, recognized globally, stands out by providing certified trainers, hands-on training, and industry recognition, ensuring students receive high-quality education aligned with industry standards.

The Certified Data Scientist (CDS) course in Baner is a comprehensive training program designed for individuals aspiring to enter the field of Data Science. Covering essential concepts, tools, and techniques, the course equips participants to excel in the dynamic realm of data science.

DataMites provides Data Science Offline Training in two Pune locations: Baner, and Kharadi. This strategic placement ensures accessibility for individuals across different areas of the city, making it convenient for aspiring data scientists to pursue their training.

The Certified Data Scientist Training is designed for data science enthusiasts aiming to acquire essential skills and knowledge for success in the field. While specific prerequisites may vary, a passion for data science and a willingness to learn are often the key criteria for enrollment.

DataMites offers a diverse range of Data Science courses, including Data Science Foundation, Data Science for Managers, Data Science Associate, Diploma in Data Science, Python for Data Science, Certified Data Scientist, Statistics for Data Science, and industry-specific courses. This variety allows individuals to choose a program aligned with their specific career goals.

The DataMites Data Science Training Fee in Baner can vary from Rs.28,000 to Rs.88,000. The fee fluctuation reflects different courses and training modes. Choosing a course that aligns with both budget and educational requirements is crucial, considering education as an investment in future career opportunities.

Yes, DataMites provides classroom training for their Data Science courses in Baner. This interactive learning experience allows students to engage with instructors, collaborate with peers, and gain practical insights, enhancing the overall learning experience.

The Flexi-Pass from DataMites allows attendees three months of access to Data Science training sessions. This flexibility benefits individuals needing additional time for topic revision, practice, and query resolution, ensuring a comprehensive understanding of the course material.

Certainly, DataMites offers placement assistance to students completing their data science courses. The Placement Assistance Team provides support in various aspects of career development, including guidance, resume building, and interview preparation, aiding students in securing desirable positions in the field of Data Science.

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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