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: PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity
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
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions
MODULE 5: PYTHON NUMPY PACKAGE
• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations
MODULE 6: PYTHON PANDAS PACKAGE
• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis
MODULE 1: DATA SCIENCE ESSENTIALS
• Introduction to Data Science
• Data Science Terminologies
• Classifications of Analytics
• Data Science Project workflow
MODULE 2: DATA ENGINEERING FOUNDATION
• Introduction to Data Engineering
• Data engineering importance
• Ecosystems of data engineering tools
• Core concepts of data engineering
MODULE 3: PYTHON FOR DATA SCIENCE
• Introduction to Python
• Python Data Types, Operators
• Flow Control statements, Functions
• Structured vs Unstructured Data
• Python Numpy package introduction
• Array Data Structures in Numpy
• Array operations and methods
• Python Pandas package introduction
• Data Structures : Series and DataFrame
• Pandas DataFrame key methods
MODULE 4: VISUALIZATION WITH PYTHON
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations
MODULE 5: R LANGUAGE ESSENTIALS
• R Installation and Setup
• R STUDIO – R Development Env
• R language basics and data structures
• R data structures , control statements
MODULE 6: STATISTICS
• Descriptive And Inferential statistics
• Types Of Data, Sampling types
• Measures of Central Tendencies
• Data Variability: Standard Deviation
• Z-Score, Outliers, Normal Distribution
• Central Limit Theorem
• Histogram, Normality Tests
• Skewness & Kurtosis
• Understanding Hypothesis Testing
• P-Value Method, Types Of Errors
• T Distribution, One Sample T-Test
• Independent And Relational T Tests
• Direct And Indirect Correlation
• Regression Theory
MODULE 7: MACHINE LEARNING INTRODUCTION
• Machine Learning Introduction
• ML core concepts
• Unsupervised and Supervised Learning
• Clustering with K-Means
• Regression and Classification Models.
• Regression Algorithm: Linear Regression
• ML Model Evaluation
• Classification Algorithm: Logistic Regression
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification, And Regression
• Supervised Vs Unsupervised
MODULE 2: ML ALGO: LINEAR REGRESSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 3: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 4: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 5: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 7: ML ALGO: DECISION TREE
• Random Forest Ensemble technique
• How it works: Bagging Theory
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 9: GRADIENT BOOSTING, XGBOOST
• Introduction to Boosting and XGBoost
• How it works: weak learners' concept
• Modeling and Evaluation of in Python
MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN)
• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python
MODULE 12: ADVANCED ML CONCEPTS
• Adv Metrics (Roc_Auc, R2, Precision, Recall)
• K-Fold Cross-validation
• Grid And Randomized Search CV In Sklearn
• Imbalanced Data Set: Smote Technique
• Feature Selection Techniques
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Autoregressive Model (AR)
• Moving Average Model (MA)
• Stationarity of Time Series
• ARIMA Model
• Autocorrelation and AIC
MODULE 2: FEATURE ENGINEERING
• Introduction to Features Engineering
• Transforming Predictors
• Feature Selection methods
• Backward elimination technique
• Feature importance from ML modeling
MODULE 3: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• Python packages: TextBlob, NLTK
• Case study: Twitter Live Sentiment Analysis
MODULE 4: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 5: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model Deployment
MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Pivot Table
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Goal Seek Analysis
• Data Table
• Solving Data Equation with EXCEL
• Monte Carlo Simulation with MS EXCEL
MODULE 7: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure, AWS
• AWS Service ( EC2 and S3 service)
• AWS Service (AMI), AWS Service (RDS)
• AWS Service (IAM), AWS (Athena service)
• AWS (EMR), AWS, AWS (Redshift)
• ML Modeling with AWS Sage Maker
MODULE 8: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline and ML modeling with Azure
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
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo
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
MODULE 5: UNDOING CHANGES
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 6: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account
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
• Hands-on Map Reduce task
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
• Working with Spark SQL Query Language
MODULE 5: MACHINE LEARNING WITH SPARK ML
• Introduction to MLlib Various ML algorithms supported by MLib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest
MODULE 6: KAFKA and Spark
• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations
MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION
• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology
MODULE 2: BI WITH TABLEAU: INTRODUCTION
• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing
MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE
• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs
MODULE 4: TABLEAU: BUSINESS INSIGHTS
• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• Parameters
• The Formatting Pane
• Trend Lines & Reference Lines
• Forecasting
• Clustering
MODULE 5: DASHBOARDS, STORIES AND PAGES
• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages
MODULE 6: BI WITH POWER-BI
• Power BI basics
• Basics Visualizations
• Business Insights with Power BI
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
• Comments
• import and export dataset
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
• Cross join
• Self join
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
• MongoDB data management
MODULE 1: ARTIFICIAL INTELLIGENCE OVERVIEW
• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence.
• Why Artificial Intelligence Now?
• Ai Terminologies
• Areas Of Artificial Intelligence
• Ai Vs Data Science Vs Machine Learning
MODULE 2: DEEP LEARNING INTRODUCTION
• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks
MODULE 3: TENSORFLOW FOUNDATION
• TensorFlow Installation and setup
• TensorFlow Structure and Modules
• Hands-On: ML modeling with TensorFlow
MODULE 4: COMPUTER VISION INTRODUCTION
• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network
MODULE 5: NATURAL LANGUAGE PROCESSING (NLP)
• NLP Introduction
• Bag of Words Models
• Word Embedding
• Language Modeling
• Hands-On: BERT Algorithm
MODULE 6: AI ETHICAL ISSUES AND CONCERNS
• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai: Ethics, Bias, And Trust
Data science is an interdisciplinary field that involves extracting insights and knowledge from data using various techniques such as statistical analysis, machine learning, and data visualization.
Learning data science is important because it equips individuals with the skills to extract valuable insights from data, make data-driven decisions, and solve complex problems across industries.
Effective methods for learning data science include online courses, bootcamps, self-study through books and tutorials, and practical application of skills on real-world projects.
Some common challenges encountered by data scientists include data quality issues, dealing with large and complex datasets, selecting appropriate models and algorithms, and interpreting and communicating results effectively.
The cost of a data science course in Dehradun can vary depending on the institution and program, but it generally ranges from INR 40,000 to INR 50,000.
The eligibility criteria for enrolling in a data science course can vary, but typically it requires a bachelor's degree in a relevant field (such as computer science, mathematics, or statistics) and a basic understanding of programming and statistics.
The scope of data science is vast and expanding rapidly, with increasing demand in various industries like finance, healthcare, e-commerce, and technology, offering opportunities for data scientists to solve complex problems, make informed decisions, and drive innovation.
Certification in data science is significant as it provides a recognized validation of one's skills and knowledge in the field, enhancing career prospects and credibility among employers and peers.
Yes, there is a significant demand for data science courses due to the increasing need for data-driven insights and decision-making in organizations across industries.
Pursuing a career in data science is generally considered safe due to the growing demand for data scientists, the wide range of industries that require their expertise, and the potential for career growth and attractive salary prospects
Data science can be challenging as it requires a combination of skills from various disciplines, including programming, statistics, and machine learning. However, with dedication and continuous learning, it is an achievable field of study.
Python is a powerful programming language commonly used in data science due to its extensive libraries and frameworks for data manipulation, analysis, and machine learning. However, proficiency in other tools and languages like R and SQL can also be beneficial in certain data science contexts.
SQL is often used for managing and querying relational databases, which can be an important skill for data scientists working with structured data. While not a strict requirement, familiarity with SQL can be valuable for accessing and manipulating data efficiently.
A solid understanding of statistics is necessary for data science as it forms the foundation for various statistical techniques, hypothesis testing, and model evaluation. Statistical knowledge helps in making informed decisions and drawing meaningful insights from data.
Necessary skills for becoming a data scientist include proficiency in programming languages (such as Python or R), statistical analysis, machine learning, data manipulation, and data visualization.
DataMites stands out as an excellent choice for a Data Science course in Dehradun due to its skilled faculty, comprehensive curriculum that encompasses diverse data science subjects, emphasis on practical learning with hands-on experience, industry-oriented projects, and dedicated support for placement opportunities.
DataMites Certified Data Scientist Course in Dehradun is open to individuals with a strong foundation in mathematics and programming, as well as those with previous experience in statistics, engineering, or related disciplines. This inclusive approach caters to a diverse range of participants who have aspirations to establish a thriving career in the dynamic field of Data Science.
One should opt for the DataMites data science course in Dehradun for its meticulously designed curriculum, experienced faculty, interactive hands-on learning opportunities, practical project assignments, and industry-focused training. This comprehensive program can significantly enhance your knowledge and skills in data science, thereby improving your job prospects in the field.
The course duration spans over 8 months with 700 learning hours, which includes 120 hours of live online training.
Upon completion of the data science course in Dehradun, students receive the prestigious IABAC certification, widely acknowledged globally. This certification serves as a valuable credential, enhancing employment prospects and enabling participation in internship programs, thereby significantly enriching opportunities in the field of data science.
DataMites provides strong support and guidance for placements through their dedicated Placement Assistance Team (PAT) upon successful completion of the course. The personalized assistance offered by PAT ensures individuals receive comprehensive support in securing suitable job placements, significantly boosting their employment prospects and opening up a multitude of opportunities.
DataMites provides a wide array of data science courses in Dehradun that encompass diverse areas such as Data Science Foundation, Data Science for Managers, Data Science Associate, Diploma in Data Science, Python for Data Science, Statistics for Data Science, Data Science Marketing, Data Science Operations, Data Science Retail, Data Science for HR, Data Science with Finance, and Data Science.
DataMites is widely recognized for its team of highly experienced and knowledgeable educators who are industry experts in the field of data science. With their exceptional qualifications, certifications, and extensive expertise, these instructors deliver outstanding instruction, empowering students to develop a comprehensive understanding of the subject matter.
DataMites offers flexible learning options to accommodate the preferences of students, providing a range of choices such as live online sessions, self-paced learning, and on-demand classroom training. This flexibility empowers individuals to choose the learning approach that best fits their needs, making it convenient for them to pursue their data science education.
DataMites provides a comprehensive overview of its training approach and offers a complimentary demo class, giving students an opportunity to fully grasp the training process and its components. This allows individuals to evaluate the quality and suitability of the training before making a commitment.
Learning Through Case Study Approach
Theory → Hands-on → Case Study → Project → Model Deployment
The payment mode available for the data science course in Dehradun through:
DataMites offers its Data Science Course in Dehradun at various price points, providing options such as INR 35,000 for live online training, INR 21,000 for blended learning, and INR 44,000 for on-demand classroom training. This flexible pricing structure allows individuals to choose the plan that best suits their budget and preferred mode of learning.
To acquire the participation certificate and book the certification exam, it is mandatory to present valid photo identification proofs such as a National ID card or a Driving license. These identification proofs are crucial in ensuring the authenticity and accuracy of the certification process.
The salary of a data scientist in India ranges from INR 11,30,556 per year according to a Glassdoor report.
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.