Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
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.
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
The cost of a Data Science course in Salem varies from INR 20,000 to INR 2,00,000, depending on the institute and course level. Advanced programs or certifications may cost more.
According to AmbitionBox reports, the salary of a Data Scientist in Salem varies between INR 3 Lakhs and INR 11 Lakhs per year. On average, professionals in this role earn an annual salary of approximately INR 8 Lakhs. The exact pay depends on factors such as experience, skills, and the employing organization.
The best approach is to take a structured course covering Python, machine learning, and statistics. Hands-on practice with real-world datasets enhances learning. Self-study through online platforms can also be beneficial.
Data Science courses in Salem typically last 3 to 12 months. Short-term certifications take around 3–6 months, while diploma or PG programs may extend to a year. Practical projects influence course duration.
Data Science has strong growth prospects in Salem with increasing demand across industries. Businesses rely on data-driven decisions, creating more opportunities. Expertise in AI and machine learning can further boost career prospects.
Most courses require a background in mathematics, statistics, or computer science. Graduates from any discipline with basic analytical skills can apply. Some advanced courses may have prerequisites in coding and data handling.
Several institutes in Salem offer Data Science courses with various specializations, but the Certified Data Scientist Course is one of the best. A good course should cover Python, machine learning, and hands-on projects. Opt for one that provides industry-relevant content and practical exposure.
Basic coding skills are essential for Data Science, especially in Python or R. Many tools simplify tasks, but understanding programming improves efficiency. Learning fundamental coding concepts helps in long-term success.
Strong analytical thinking, statistical knowledge, and programming skills are key. Familiarity with machine learning, data visualization, and SQL is essential. Problem-solving and communication skills are also important.
Yes, non-engineering graduates can enter Data Science with proper training. A strong foundation in mathematics, statistics, or programming helps. Many professionals from diverse fields transition into Data Science successfully.
The demand for Data Scientists in Salem is growing across industries like healthcare, retail, and IT. Skilled professionals with expertise in AI and machine learning have better opportunities. Freelancing and remote jobs also offer career options.
Several institutes in Salem offer quality Data Science training, with Datamites being one of the best. The ideal institute should have experienced faculty, a well-structured curriculum, and industry-relevant content. Opt for one that provides strong placement support and practical project experience.
A Data Scientist should be proficient in Python, machine learning, and statistics. Understanding data visualization, databases, and cloud computing is beneficial. Continuous learning and problem-solving skills are crucial.
Handling large datasets, ensuring data quality, and selecting the right algorithms are common challenges. Interpreting results accurately and communicating insights effectively require expertise. Keeping up with evolving technology is essential.
Python is widely used for data analysis, visualization, and machine learning. Its vast libraries, like Pandas, NumPy, and Scikit-learn, simplify tasks. Python's ease of learning and community support make it ideal for Data Science.
A certified Data Science course covers Python, statistics, machine learning, and data analysis. It includes practical projects, case studies, and real-world applications. Some programs offer industry-recognized certification for better job prospects.
Data Science involves predictive modeling, machine learning, and big data handling. Data Analytics focuses on interpreting historical data for business insights. Data Science is broader, incorporating AI and automation.
Salem’s most vibrant neighborhoods include Fairlands (636016), a prime residential and commercial hub, and Gugai (636006), known for its bustling markets and connectivity. Hasthampatti (636007) offers a blend of modern living and essential amenities, while Ammapet (636003) is a thriving locality with a strong business presence. Seelanaickenpatti (636201) and Dadagapatti (636006) are rapidly developing with improved infrastructure. Emerging areas like Meyyanur (636004), Suramangalam (636005), and Kondalampatti (636010) provide excellent residential and business opportunities, making Salem a dynamic and growing city for families and professionals alike.
A Data Science course includes Python, statistics, machine learning, data visualization, and SQL. It also covers deep learning, big data technologies, and cloud computing. Hands-on projects and case studies are essential components.
Data Science comprises data collection, cleaning, analysis, modeling, and visualization. It involves programming, statistics, and domain knowledge for meaningful insights. Ethical data handling and communication skills are also important.
Yes, DataMites in Salem offers EMI options for their Data Science course fees, making it easier to manage the cost of the program. They also accept various payment methods, including credit cards, debit cards, and online payments. For detailed information on EMI terms and conditions, please contact DataMites directly.
To enroll in DataMites' Data Science course in Salem, visit our official website and complete the registration form. For assistance, you can contact their support team. Enrollment typically requires providing personal details and payment information.
The Data Science course in Salem is available with fees ranging from INR 34,951 to INR 64,451, depending on the learning mode. The Live Virtual Instructor-Led Online Training is priced at INR 59,451, while the Classroom In-Person Training costs INR 64,451. The Blended Learning option, which includes self-paced study with live mentoring, is offered at INR 34,951.
Yes, DataMites Salem offers a Data Science course that includes an internship. The program provides hands-on experience to help learners apply their knowledge in real-world projects. DataMites ensures quality training with practical exposure to industry-related challenges.
DataMites offers a comprehensive Data Science course in Salem with expert trainers, hands-on projects, and industry-recognized certifications. The curriculum is designed to equip learners with practical skills in data analytics, machine learning, and AI. With flexible learning options and career support, DataMites ensures a strong foundation for a successful data science career.
The DataMites Data Science course in Salem spans 8 months, encompassing 700 hours of comprehensive learning. This includes 120 hours of live online training, covering key areas such as artificial intelligence, machine learning, data analytics, and deep learning. The program is designed to provide flexible learning options, allowing you to study at your convenience.
DataMites offers free demo classes to prospective students, allowing them to experience the quality of training before enrollment. These sessions provide insights into the course structure and teaching methodology. To schedule a demo class, please contact DataMites directly.
DataMites in Salem offers a Data Science course that includes placement support. The program features comprehensive training, live projects, and internships to enhance practical skills. Upon successful completion, students receive assistance with job search strategies, resume building, and interview preparation to help secure relevant positions in the field.
Yes, DataMites Salem offers courses that include live projects to provide hands-on experience. These projects help learners apply their skills in real-world scenarios. DataMites ensures practical learning to enhance industry readiness.
DataMites in Salem provides various payment options, including credit and debit cards (Visa, MasterCard, American Express), net banking, PayPal, and cash payments. EMI options are available for credit card transactions. A token advance is required at registration, with the remaining amount payable before course completion.
DataMites offers a 100% money-back guarantee if a refund request is made within one week from the batch start date, provided the participant has attended at least two sessions and accessed no more than 30% of the study material. Refund requests should be sent from the registered email to care@datamites.com. Please note, no refunds are issued after six months from the course enrollment date.
Yes, DataMites Salem provides course certification accredited by IABAC® and NASSCOM® FutureSkills. The certification validates your expertise and enhances career opportunities in the field. DataMites ensures industry-recognized credentials to help you stand out professionally.
DataMites Salem provides comprehensive study materials, including video tutorials, e-books, and hands-on practice datasets. Learners also benefit from real-world case studies and interactive live projects for practical exposure. These resources help build a strong foundation in data science, AI, and machine learning.
The DataMites Data Science syllabus covers key topics such as Python programming, machine learning, and data visualization. It also includes deep learning, artificial intelligence, and model deployment for real-world applications. DataMites ensures a structured learning path with hands-on projects and industry-relevant skills.
The DataMites Flexi-Pass offers a 3-month flexible duration to attend Data Science training as per convenience. It helps learners revisit sessions, resolve doubts, and reinforce their knowledge. With DataMites, this approach ensures continuous support for effective 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.