4.9 (28,680) 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!
According to AmbitionBox, Data Scientist salaries in Vizag range from INR 4 Lakhs to INR 24 Lakhs annually, with an average of INR 7.5 Lakhs.
Anyone with an interest in data science, including students, professionals, and fresh graduates, can enroll. Basic knowledge of mathematics, statistics, and programming is beneficial. Some courses may have specific prerequisites based on their level.
The duration of data science courses varies from a few weeks to several months. Short-term certification courses last 3-6 months, while diploma and degree programs can take 1-2 years. The course length depends on depth, mode (online/offline), and learning pace.
Most courses require candidates to have a background in science, engineering, or commerce with mathematics. Basic programming and analytical skills are preferred but not always mandatory. Advanced programs may require prior experience or technical education.
The Data Science course fee in Vizag generally ranges between Rs 15,000 and Rs 2,50,000, depending on the institute, program duration, and learning format. Learners can choose from online, classroom, or self-paced options based on their convenience and preferred learning style.
The scope of data science in Vizag is expanding with rising demand across IT, AI, and analytics industries, supported by government-backed tech hubs. The future outlook is promising as more companies invest in data-driven solutions, creating strong career opportunities in the city.
Essential technical skills include Python, R, SQL, and machine learning. Knowledge of statistics, data visualization, and big data tools like Hadoop and Spark is beneficial. Cloud computing and AI expertise can enhance career prospects.
The best course depends on factors like curriculum, faculty, hands-on training, and industry relevance. The Certified Data Scientist course is one of the best, as it offers real-world projects, recognized certifications, and strong placement support. Both online and offline options are available to suit your preference.
Yes, data scientist roles remain in high demand in India, with major reports highlighting a surge in vacancies across finance, e-commerce, edtech, and healthcare. The demand is rising faster than the supply of talent, creating a strong job market. India’s data science industry is projected to touch $20 billion by 2025, and this rapid growth means skilled professionals will continue to find excellent career opportunities.
Yes, non-engineering graduates can enter data science with relevant skills and certifications. A background in mathematics, statistics, or programming is helpful. Many professionals from different fields transition into data science with proper training.
A data scientist needs strong analytical skills, programming knowledge (Python, R, SQL), and statistical expertise. Machine learning, data visualization, and problem-solving abilities are crucial. Soft skills like communication and critical thinking are also important.
In Vizag, several institutes offer quality data science training. DataMites is among the top choices, providing globally recognized certifications, experienced faculty, and hands-on learning opportunities. Their comprehensive curriculum is designed to equip students with practical skills for a successful career in data science.
A structured approach combining online courses, classroom training, and self-study works best. Hands-on practice through projects, internships, and Kaggle competitions enhances learning. Staying updated with industry trends and networking helps in career growth.
Yes, Python is widely used in data science due to its ease of use and extensive libraries. However, other languages like R and SQL are also important. Learning Python provides a strong foundation for data analysis and machine learning.
Data science involves data collection, cleaning, analysis, machine learning, and visualization. Statistical modeling, AI, and big data technologies play a significant role. The goal is to derive insights and drive decision-making through data.
Basic coding proficiency is essential, especially in Python, R, or SQL. Advanced roles may require knowledge of machine learning algorithms and big data tools. Some roles, like data analysts, require less coding and focus more on analytics.
Popular tools include Python, R, SQL, Tableau, Power BI, and Excel for analysis. Machine learning frameworks like TensorFlow, Scikit-learn, and PyTorch are widely used. Big data technologies like Hadoop, Spark, and cloud platforms are also essential.
Yes, many data science institutes in Vizag provide placement support to help learners transition into industry roles. Services typically include resume building, mock interviews, and connections with hiring companies, ensuring students are job-ready after completing their training.
Vizag features prime areas like Dwaraka Nagar (530016), Maddilapalem (530013), and Seethammadhara (530013), which are well-known residential and commercial hubs. Localities such as MVP Colony (530017), Akkayyapalem (530016), and Gajuwaka (530026) make access to data science training convenient. The branch is also easily reachable from nearby areas like Pendurthi (530041), Simhachalam (530028), and Madhurawada (530048), with Vizag’s well-connected neighborhoods supporting students and professionals in pursuing practical learning.
Yes, online data science training is available in Vizag, offering flexible options like live virtual classes and self-paced learning. These programs allow learners to access quality training from anywhere while balancing studies with personal or professional commitments.
DataMites in Vizag offers a comprehensive Data Science course that includes an internship opportunity. This program provides practical experience under the guidance of industry experts, enhancing your skills in real-world applications. Upon completion, DataMites awards both a course completion certificate and an internship experience letter.
Learners from several prime areas of Vizag can easily enroll in DataMites courses. Students from Dwaraka Nagar (530016), Maddilapalem (530013), Seethammadhara (530013), MVP Colony (530017), Akkayyapalem (530016), Gajuwaka (530026), and nearby regions like Pendurthi, Simhachalam, and Madhurawada can easily access the center. DataMites Vizag is located at Unit No 601, Mybranch, 5th Floor, Grand Palace, Sagar Nagar, Dwaraka Nagar, Visakhapatnam – 530016.
Yes, DataMites offers a free demo class for their Data Science course. This session provides an opportunity to experience their teaching approach and course structure. You can book the demo through our official website.
DataMites offers a Data Science course in Vizag with the following fee structure:
These fees are subject to change; for the most current information, please visit the official DataMites website.
DataMites in Vizag offers EMI options for their Data Science courses, allowing students to pay fees in installments. Payments can be made online via Razorpay, and selecting a credit card enables the EMI option. A token advance is required during registration, with the remaining balance due before course completion.
Yes, DataMites Vizag offers a Data Science course with placement assistance, including internships, projects, and career support. While they help with job opportunities, placements are not guaranteed and depend on individual performance.
DataMites offers Data Science courses in Vizag, led by experienced trainers with extensive industry backgrounds. The lead mentor, Ashok Veda, brings 19 years of experience in analytics and data science. He has trained over 20,000 data science aspirants and currently serves as the Founder and CEO at Rubixe.com, an AI company.
DataMites' Data Science courses in Vizag are open to individuals interested in building a career in data science, regardless of their educational background. Both beginners and professionals looking to upgrade their skills can enroll. The courses are designed to cater to diverse learning needs and aspirations in the field of data science.
DataMites offers comprehensive data science training with a focus on practical skills and real-world applications. Their expert instructors provide personalized guidance, ensuring a deep understanding of key concepts. With flexible learning options and strong industry connections, DataMites supports your career growth in data science.
DataMites Vizag provides a variety of payment options for course fees, such as credit/debit cards, net banking, PayPal, and cash. EMI options are available for credit card payments. A token advance is required at the time of registration, with the remaining balance due before course completion.
The Data Science course at DataMites in Vizag spans 8 months, providing comprehensive learning. It consists of more than 700 hours of structured content. This duration is designed to give students a deep understanding of the subject.
DataMites offers both online and offline classes in Vizag, catering to different learning preferences. Students can opt for virtual sessions or attend in-person classes depending on their convenience. The flexible options ensure a wide reach for learners in the region.
DataMites offers courses that include hands-on experience through live projects. These projects provide practical exposure, allowing learners to apply their skills in real-world scenarios. This approach enhances learning and helps build industry-relevant expertise.
DataMites offers a 100% money-back guarantee if a refund request is made within one week from the batch start date, provided the candidate attends at least two sessions and accesses less than 30% of the material. Refunds are processed within 15 to 17 business days. Note that exam bookings are non-refundable, and refunds are not available after six months from enrollment.
Yes, DataMites provides course certification upon successful completion of its programs. The certification is accredited by recognized bodies such as IABAC and NASSCOM FutureSkills. This ensures that the certification is widely respected and valued in the industry.
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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