4.8 (21,000) reviews 110,000
59,451*
NO COST EMI : 9,909 p.m for 6 Months
*Offer valid till 12th 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!
To be eligible for data science in Guwahati, candidates typically need a bachelor's degree in engineering, mathematics, statistics, or a related field. Strong analytical skills and knowledge of programming languages like Python or R are often required. Some programs may also prefer prior experience in data analysis or machine learning.
Data Science courses in Guwahati usually range from 3 months to 1 year, depending on the program’s format and depth of coverage. Short-duration courses focus more on practical, skill-based training, whereas longer programs combine in-depth theoretical learning with industry-relevant applications. The overall duration also varies with the mode of study selected, whether it is full-time, part-time, or delivered through online platforms.
According to AmbitionBox, Data Scientist salaries in Guwahati range from INR 3 Lakhs to INR 24.8 Lakhs annually, with an average of around INR 13.8 Lakhs per year.
The scope of data science in Guwahati is rapidly growing, with increasing demand across sectors like finance, healthcare, and e-commerce. With supportive educational programs and smart city initiatives, the city offers promising career opportunities and a strong future outlook for data professionals.
The Certified Data Scientist Course is a leading Data Science program in Guwahati. It offers comprehensive training in machine learning, deep learning, statistics, and hands-on projects, equipping learners with industry-relevant skills. This certification boosts career opportunities by preparing professionals for data-driven roles across diverse sectors.
The cost of a Data Science course in Guwahati typically ranges from INR 15,000 to INR 2,50,000. Fees depend on factors like course duration, curriculum depth, mode of learning, and the certification offered. Students should consider their learning objectives and budget to select a program that best meets their needs.
To study data science in Guwahati, start with online courses and self-paced learning resources covering Python, statistics, and machine learning. Join local tech meetups, workshops, or hackathons to gain practical experience and network with professionals. Work on real-world projects and internships to build a strong portfolio and apply your skills effectively.
Several institutes in Guwahati offer quality data science training with practical learning. Among them, DataMites stands out for its structured curriculum, industry-relevant projects, and expert mentorship. It provides a strong foundation for aspiring data scientists to build successful careers.
Yes, non-engineering graduates can join data science courses in Guwahati. Many programs accept students from diverse backgrounds, including mathematics, statistics, and economics. Basic knowledge of programming and analytics can be beneficial for better understanding.
A data science career requires strong analytical skills, proficiency in programming (such as Python or R), and a solid understanding of statistics and machine learning. Data wrangling, data visualization, and problem-solving abilities are also essential. Effective communication skills help in conveying insights clearly to stakeholders.
Data science job opportunities in Guwahati remain robust, with a variety of positions available across multiple sectors. Recent listings show roles such as Data Analysis and Visualization Application Developer, MIS Analyst, and Research Analyst in demand. This trend indicates a sustained need for data professionals in the region.
Coding proficiency is important in data science, but the required level depends on the role. Analysts may use minimal coding, while machine learning and data engineering roles demand strong programming skills. Learning coding enhances problem-solving and career growth in data science.
The main ethical concerns in data science include privacy, bias, and transparency. Protecting personal data, ensuring fair and unbiased algorithms, and clearly explaining how data is used are essential. Responsible practices help build trust and prevent harm.
In Guwahati, data science plays a key role in industries like e-commerce, banking, and healthcare by enhancing customer insights and decision-making. The logistics and transportation sector relies on data analytics for route optimization and operational efficiency. Additionally, government and environmental organizations use data science for urban planning and resource management.
Data science relies on programming languages like Python and R for analysis, along with SQL for managing databases. Key tools include Jupyter Notebooks, TensorFlow for machine learning, and Tableau for data visualization. Cloud platforms such as AWS and Google Cloud enhance scalability and data processing.
SQL is essential for data science as it helps efficiently extract, manipulate, and analyze large datasets stored in databases. It enables data cleaning, transformation, and retrieval, which are crucial for building accurate models. Mastering SQL improves workflow efficiency and ensures seamless integration with various analytical tools.
A data scientist course trains learners to build models, develop predictive algorithms, and extract insights from complex datasets. While data analysts focus on interpreting data to identify trends, data scientists create solutions that drive business strategy. In essence, analysts provide insights, and data scientists turn them into actionable innovations.
Statistical analysis is essential in data science as it helps in understanding data patterns, making informed predictions, and validating results. It enables data-driven decision-making by providing insights into the significance and relationships within data. This process ensures accuracy and reliability in data interpretation.
Some of the key challenges in data science involve processing and structuring massive datasets, handling incomplete or inconsistent information, maintaining data security and privacy, choosing suitable algorithms, and converting advanced analytical insights into actionable business strategies.
To become a data scientist in Guwahati, start by gaining a solid foundation in mathematics, statistics, and programming. Next, build expertise in data analysis tools and machine learning algorithms. Finally, gain hands-on experience through projects or internships to apply your skills in real-world scenarios.
Yes, EMI options are available for the data science course in Guwahati. This allows candidates to conveniently pay their fees in installments. Please inquire directly with the institution for detailed payment plans.
To enroll in the Data Science course, visit the official website and fill out the registration form. Ensure all required documents are submitted with the application. Once processed, you will receive confirmation and further instructions on course details.
The Data Science course in Guwahati offers three learning options:
Yes, there are data science courses available in Guwahati that include an internship opportunity. These courses typically combine theoretical learning with practical experience. Internships are designed to enhance hands-on skills for real-world applications in data science.
Choosing a data science course in Guwahati ensures access to expert trainers and industry-aligned curriculum tailored to real-world applications. It offers hands-on learning opportunities and practical exposure to data tools. Moreover, the course is designed to equip you with skills that are in high demand in the job market.
The Data Science course at DataMites Guwahati spans 8 months, offering a comprehensive learning experience. It includes a total of 700 hours of in-depth training. This structure is designed to provide students with the necessary skills and knowledge for a successful career in data science.
DataMites Guwahati offers a free demo class for those interested in exploring data science. This allows potential learners to experience the content and teaching style before committing. The program spans 8 months, covering 700 hours of comprehensive learning.
Yes, DataMites Guwahati offers a Data Science course that includes placement assistance. The program provides hands-on training, live projects, and career support to help learners secure data-focused roles.
DataMites offers various payment methods for course enrollment, such as debit/credit cards (Visa, MasterCard, American Express) and PayPal. Once payment is processed, you will receive confirmation along with the necessary course materials. DataMites also provides assistance to help you through the payment procedure if needed.
DataMites offers a data science course that includes hands-on learning through live projects. The course is designed to provide practical experience, enabling learners to apply theoretical knowledge. This approach helps build real-world skills in data science.
DataMites Guwahati provides a variety of study materials, including comprehensive course modules, interactive video lessons, and real-world case studies. These resources are designed to enhance learning and provide practical insights. Additionally, learners have access to hands-on projects and assessments to reinforce their understanding.
Yes, DataMites Guwahati provides course certification upon successful completion of the Data Science program. The certification is awarded by reputed organizations such as IABAC® and NASSCOM® FutureSkills. This credential validates your expertise and strengthens career prospects in the field of data science.
The DataMites data science syllabus covers a range of essential topics including statistical analysis, machine learning, data visualization, and big data technologies. It also introduces tools such as Python, R, and SQL for data manipulation. The curriculum is designed to equip learners with both theoretical knowledge and practical skills in data science.
DataMites offers a 100% money-back guarantee if you request a refund within one week of the course start date and attend at least two sessions during the first week. Refunds are unavailable after six months or if more than 30% of the course material has been accessed. To request a refund, email care@datamites.com from your registered email.
DataMites Flexi-Pass offers a 3-month period to attend Data Science sessions in Surat at your convenience. It allows learners to revisit sessions, resolve doubts, and strengthen their knowledge. This flexible approach ensures ongoing support and a personalized learning experience.
DataMites Guwahati is easily accessible from key areas such as Paltan Bazaar (781001) and Fancy Bazar (781001), popular commercial zones, and Uzan Bazar (781001), home to several educational institutes. Residential and mixed-use neighborhoods like Chandmari (781003), Zoo Road (781024), and Santipur (781009) provide convenient access to training centers. The branch is also well connected to nearby localities including Kumarpara (781001/781009), Beltola (781028), Ganeshguri (781005), Dispur (781005), Hatigaon (781038), Kahilipara (781019), Garchuk (781035), Guwahati Airport (Borjhar) (781015), Guwahati University (Jalukbari) (781014), and North Guwahati (781030), making it easy for students and professionals to attend data science courses.
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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