4.9 (26,680) reviews 110,000
59,451*
NO COST EMI : 9,909 p.m for 6 Months
*Offer valid till 31st October 2025
Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
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
Enrolling at DataMites was one of the best decisions I made for my career. Coming from a freelance writing background with no prior technical experience, I was initially unsure about stepping into the field of data analytics. But DataMites made the transition smooth and achievable. I’d highly recommend them to anyone looking for a guided, industry-ready data analytics course. A true DataMites success story!
As per AmbitionBox, Data Scientists in Indore have a salary range of INR 3 Lakhs to INR 19 Lakhs per year, with an average annual income of INR 11 Lakhs for those with 1 to 6 years of experience, highlighting it as a lucrative career option.
Anyone with a basic understanding of mathematics and statistics can enroll. The course suits recent graduates, working professionals seeking upskilling, career switchers, and even non-IT graduates. Prior programming knowledge is helpful but not mandatory.
Data science courses in Indore vary in duration based on the type of program. Short-term certification courses typically last between 6 to 8 months, offering intensive training. In contrast, full-time degree programs, such as a B.Sc. in Data Science, span over 3 to 4 years, providing comprehensive academic education.
To study data science in Indore, start by mastering Python, statistics, and machine learning through online courses and hands-on projects. Join local tech meetups, participate in hackathons, and engage with the data science community for practical exposure. Work on real-world datasets, build a strong portfolio, and stay updated with industry trends to enhance your skills.
Several institutes in Indore offer quality data science training, but DataMites stands out with its comprehensive curriculum, practical projects, industry-recognized certifications, expert mentorship, and placement support. Other reputable institutes in the city also provide specialized courses in AI, machine learning, and Python programming.
Data science in Indore has strong growth potential, with rising demand across IT, finance, healthcare, and e-commerce. Opportunities in AI, machine learning, and analytics make it a promising career for both freshers and experienced professionals.
The Certified Data Scientist Course is one of the top data science programs available in Indore. It covers machine learning, deep learning, statistics, and hands-on projects, equipping learners with industry-relevant skills. This certification boosts career prospects by preparing students for data-driven roles across multiple sectors.
Yes, data science continues to be a high-demand field due to the increasing need for data-driven decision-making. Companies across various industries are seeking professionals with expertise in analyzing and interpreting data. The growth of AI, machine learning, and big data ensures strong job prospects for data scientists.
Yes, non-engineering graduates can join data science roles in Indore. Key skills like programming, statistics, and data analysis are important to transition into this field. Many companies value practical expertise and problem-solving abilities, regardless of the graduate's academic background.
To become a data scientist, proficiency in programming languages like Python and R is essential for data manipulation. A solid understanding of statistics and machine learning techniques helps in drawing insights from data. Additionally, effective communication skills are crucial for presenting findings to non-technical audiences.
Data science course fees in Indore generally range from INR 15,000 to INR 2,50,000, depending on the institute, course duration, and mode of learning. Programs are available in classroom-based, online, and hybrid formats to suit different learning preferences.
The eligibility criteria for data science courses in Indore typically include a basic understanding of mathematics and statistics and a graduation in any discipline. While prior programming knowledge is helpful, it is not mandatory, as most courses teach coding, data analysis, and machine learning fundamentals from scratch.
Learning Python is highly recommended for a data science course in Indore as it is a key programming language used in data analysis and machine learning. Python's libraries and simplicity make it an essential tool in the field. While not strictly mandatory, mastering Python enhances career prospects in data science.
Data science integrates statistical analysis, machine learning, and data visualization to extract insights from structured and unstructured data. It involves data cleaning, exploration, and model building to drive informed decisions. Effective communication of results through visual tools is essential for stakeholders' understanding.
Coding proficiency is highly beneficial for a career in data science, as it enables efficient data manipulation, analysis, and model building. While some roles may emphasize other skills, coding remains essential for handling complex datasets and automation tasks. Mastery of programming languages like Python or R can significantly enhance performance in this field.
Data science commonly utilizes programming languages like Python and R for data analysis and modeling. Tools such as Jupyter Notebook and Tableau help with data visualization and exploration. Machine learning frameworks like TensorFlow and Scikit-learn are essential for building predictive models.
In Indore, the IT and software development industries extensively use data science for software solutions and analytics. The manufacturing sector leverages data-driven insights to optimize production processes and quality control. Additionally, the retail and e-commerce industries apply data science for customer behavior analysis and inventory management.
Ethical concerns in data science include ensuring privacy and confidentiality of personal data, preventing algorithmic bias that may lead to unfair outcomes, and maintaining transparency in decision-making processes. It's essential to prioritize fairness, accountability, and the responsible use of data. Balancing innovation with respect for individuals' rights is a key challenge.
Data Engineers build and maintain data pipelines and storage systems, while Data Scientists analyze this data to create predictive models and actionable insights. Engineers manage infrastructure; scientists use the data to solve business problems.
Data science often faces challenges like handling large, messy datasets and ensuring data quality. There is also the complexity of selecting the right algorithms and models for specific tasks. Additionally, effective communication of results to stakeholders can be difficult, requiring both technical and non-technical expertise.
AI and machine learning play a crucial role in data science by enabling systems to analyze and interpret large datasets efficiently. They help automate processes, uncover hidden patterns, and make data-driven predictions. These technologies enhance decision-making and optimize operations across various industries.
Freshers in data science can pursue roles such as Data Analyst, Junior Data Scientist, BI Analyst, Data Engineer Trainee, or Machine Learning Intern, handling tasks like data cleaning, visualization, and basic predictive modeling. These positions provide practical experience and a foundation for advanced data science careers.
The latest trends in data science include the rise of AI-driven automation, enhancing predictive models with deep learning, and the growing use of real-time data processing for immediate insights. Additionally, ethical AI and data privacy concerns are becoming more central. Data scientists are also increasingly leveraging advanced tools for better visualization and decision-making.
SQL plays a crucial role in data science by enabling efficient querying and manipulation of large datasets stored in relational databases. It allows data scientists to extract, filter, and aggregate data, facilitating meaningful analysis. Mastery of SQL aids in streamlining data workflows and supporting data-driven decision-making.
Anyone interested in pursuing a career in data science is eligible to enroll in DataMites Data Science courses in Indore. The program is open to individuals with a basic understanding of mathematics and statistics. Professionals and fresh graduates looking to enhance their skills in data science are also welcome to apply.
DataMites refund policy stipulates that no refunds will be issued after six months from the course enrollment date. To qualify for a 100% refund, candidates must request it within one week from the batch start date, attend at least two training sessions during the first week, and not access more than 30% of study material or training sessions. Refund requests should be sent to care@datamites.com from the candidate’s registered email.
Yes, the DataMites Data Science Course in Indore offers internship opportunities where students work on real-world projects to gain practical experience. Internship certificates are also provided to validate the hands-on learning and enhance career prospects.
DataMites Indore provides EMI options for their data science courses, enabling students to pay fees in manageable monthly installments. Payments can be made via debit/credit cards, including Visa, MasterCard, and American Express, or through PayPal. For credit card payments, EMI options are available.
The DataMites Data Science course fees in Indore vary depending on the chosen learning mode: classroom training is approximately INR 65,000, live online sessions cost around INR 60,000, and blended learning options are available for about INR 35,000. For the latest fee details, offers, and course schedules, it is best to contact the DataMites Indore center directly.
Yes, DataMites Indore offers a Data Science course that includes placement assistance. The program features live mentoring, mock interviews, resume support, and job updates to help students secure employment. While DataMites does not guarantee a job, it provides comprehensive support to enhance career prospects.
The DataMites data science syllabus covers key topics like data analysis, machine learning, and statistical modeling. It also delves into data visualization, Python programming, and advanced AI techniques. The curriculum is designed to provide hands-on experience with real-world datasets and tools.
DataMites Indore offers both online and offline Data Science courses, allowing students to choose the learning mode that best fits their schedule and preferences. The curriculum and quality of instruction remain consistent across both formats. The Indore center is located at Virtual Coworks, 401, 4th Floor, Mangal City Mall, Scheme No 54, PU 4, Vijay Nagar, Indore, Madhya Pradesh 452010, providing convenient access for students and working professionals.
DataMites Indore is offering free data science demo sessions. These sessions provide an overview of data science concepts and career opportunities. For more details and to register, please visit our official website.
DataMites offers a comprehensive data science curriculum with practical learning opportunities. Their expert trainers provide in-depth industry insights and hands-on experience. Additionally, they have flexible training options to suit different learning preferences and schedules.
Yes, DataMites Indore offers courses that include live projects. Their Data Science course, for example, features 20 capstone projects and 1 client projects, providing practical experience under the guidance of industry experts.
A Certified Data Scientist course is a structured training program designed to equip individuals with essential skills in data analysis, machine learning, and statistical modeling. It typically includes hands-on projects and theoretical knowledge to prepare learners for real-world data science challenges. Upon completion, participants earn a certification validating their expertise in the field.
Yes, DataMites Indore offers course certification upon successful completion of their programs. The certifications are recognized by IABAC and NASSCOM FutureSkills. These credentials can help enhance your professional profile.
DataMites Indore offers various payment options for course fees, including credit/debit cards, net banking, PayPal, cash, and cheque. Similarly, DataMites Indore provides options like Visa, MasterCard, American Express, and more. For assistance or installment plans, contact service@datamites.com or call 1800-313-3434.
DataMites offers an 8-month Data Science course in Indore, comprising 120 hours of training. The program is available in both weekday and weekend sessions, allowing flexibility to suit individual schedules. This course is designed to provide comprehensive knowledge and practical experience in data science.
DataMites offers both live and recorded online classes. The live sessions allow for real-time interaction, while recorded sessions provide flexibility for later viewing. This combination ensures a comprehensive learning experience for students.
DataMites data science trainers in Indore are seasoned professionals with extensive industry experience. They offer in-depth knowledge of data science concepts and practical applications. Dedicated to comprehensive training, they provide mentorship to students.
DataMites Indore is conveniently accessible to residents from nearby areas such as Dewas (455001), Pithampur (454774), Mhow (453441), Sanwer (453551), and Rau (453331). The center is also well-connected to various localities within Indore, including Vijay Nagar (452010), Rajendra Nagar (452001), and Palasia (452001). These locations offer easy commuting options for individuals seeking to enroll in data science courses and develop expertise in emerging fields like AI and machine learning.
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: -
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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