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 ANALYSIS FOUNDATION
• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain
MODULE 2: CLASSIFICATION OF ANALYTICS
• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics
MODULE 3: CRIP-DM Model
• Introduction to CRIP-DM Model
• Business Understanding
• Data Understanding
• Data Preparation
MODULE 4: UNIVARIATE DATA ANALYSIS
• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.
MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS
• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot
MODULE 6: BI-VARIATE DATA ANALYSIS
• Scatter Plots
• Regression Analysis
• Correlation Coefficients
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
• 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
• 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 ANALYSIS
• 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: 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 6: 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: COMPARISION AND CORRELATION ANALYSIS
• Data comparison Introduction
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Performing Comparison Analysis on Data
• Performing correlation Analysis on Data
• Hands-on case study 1: Comparison Analysis
• Hands-on case study 2 Correlation Analysis
MODULE 2: VARIANCE AND FREQUENCY ANALYSIS
• Concept of Variability and Variance
• Data Preparation for Variance Analysis
• Business use cases for Variance and Frequency Analysis
• Performing Variance and Frequency Analysis
• Hands-on case study 1: Variance Analysis
• Hands-on case study 2: Frequency Analysis
MODULE 3: RANKING ANALYSIS
• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis
MODULE 4: BREAK EVEN ANALYSIS
• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Procurement Decision with break even
MODULE 5: PARETO (80/20 RULE) ANALSYSIS
• Pareto rule Introduction
• Preparation Data for Pareto Analysis
• Insights on Optimizing Operations with Pareto Analysis
• Performing Pareto Analysis on Data
• Hands-on case study: Pareto Analysis
MODULE 6: Time Series and Trend Analysis
• Introduction to Time Series Data
• Preparing data for Time Series Analysis
• Types of Trends
• Trend Analysis of the Data with Excel
• Insights from Trend Analysis
• Hands-on Case Study: Trend Analysis
MODULE 7: DATA ANALYSIS BUSINESS REPORTING
• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements
• Presenting the reports
• Hands-on case study: Create Data Analysis Reports
MODULE 1: DATA ANALYTICS FOUNDATION
• Business Analytics Overview
• Application of Business Analytics
• Visual Perspective
• Benefits of Business Analytics
• Classification of Business Analytics
• Data Sources
• Data Reliability and Validity
• Business Analytics Model
MODULE 2: OPTIMIZATION MODELS
• Prescriptive Analytics with Low Uncertainty
• Mathematical Modeling and Decision Modeling
• Break Even Analysis
• Product Pricing with Prescriptive Modeling
• Building an Optimization Model
• Case Study 1 : WonderZon Network Optimization
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity
MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION
• Mathematics beyond Linear Regression
• Hands on: Regression Modeling in Excel
• Case Study 2 : Sales Promotion Decision with Regression Analysis
• Assignment 2 : Design Marketing Decision board for QuikMark Inc.
MODULE 4: DECISION MODELING
• Prescriptive Analytics with High Uncertainty
• Comparing Decisions in Uncertain Settings
• Decision Trees for Decision Modeling
• Case Study 3 : Decision modeling of Internet Plans, Monte Carlo Simulation
• Case Study 4 : Kickathlon Sports Retailer Supplier Decision Modeling
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 REGRESSSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool
MODULE 3: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool
MODULE 4: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Hands-on KNN with ML Tool
MODULE 5: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Hands-on K Means Clustering with ML Tool
MODULE 6: ML ALGO: DECISION TREE
• Random Forest Ensemble technique
• How it works: Bagging Theory
• Hands-on Decision Tree with ML Tool
MODULE 7: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)
• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python
MODULE 9: PROJECT: PREDICTIVE ANALYTICS WITH ML
• Project Business requirements
• Data Modeling
• Building Predictive Model with ML Tool
• Evaluation and Deployment
• Project Documentation and Report
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• 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: 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
• 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: 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
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
• The Formatting Pane
• Trend Lines & Reference Lines
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: 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 analytics has revolutionized the way businesses operate, leveraging the power of data to drive informed decision-making. According to recent statistics from the Market Research Future, the global data analytics market is projected to reach a value of $132.9 billion by 2026, with a compound annual growth rate of 28.9%. This exponential growth is driven by the increasing adoption of big data analytics, artificial intelligence, and machine learning technologies across various industries.
DataMites, a leading provider of data analytics training, offers a comprehensive Data Analytics Course in Itanagar. The Certified Data Analyst Training at DataMites Institute is a comprehensive program that spans over 4 months, with more than 200 hours of learning. The course covers various topics such as statistical analysis, data visualization, machine learning, and predictive modeling. Students can expect to dedicate an average of 20 hours per week to their studies, ensuring a thorough grasp of the subject matter. One of the highlights of the Certified Data Analyst Course is the inclusion of 10 Capstone Projects and 1 Client Project. These projects allow students to apply their knowledge and skills to real-world scenarios, solving data analytics challenges and delivering practical solutions.
DataMites also offers data analytics offline courses on demand in Itanagar, providing flexibility and convenience for learners. With a strong focus on practical learning and industry relevance, DataMites offers comprehensive courses in data science, machine learning, artificial intelligence, python, MLOPs and more led by experienced faculty and industry experts. The institute stands out for its globally recognized certifications, flexible learning options, real-world projects, internship opportunities, placement assistance, and an exclusive learning community.
There are several compelling reasons to choose DataMites for Data Analytics Training in Itanagar.
Firstly, the institute boasts an experienced faculty, including industry experts like Ashok Veda, who bring their practical knowledge to the classroom.
Secondly, the course curriculum at DataMites is comprehensive, covering all essential concepts and techniques in data analytics.
Thirdly, successful completion of the course leads to globally recognized certifications from prestigious organizations such as IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing the credibility of the participants.
Moreover, DataMites offers flexible learning options, including ON DEMAND data analytics classroom training in Itanagar aswell as online data analytics training in Itanagar allowing learners to study at their own pace. Practical experience is gained through projects involving real-world data, and there is even an opportunity for data analytics internships.
The institute also provides data analytics course with placement and job references to help participants kickstart their careers.
Hardcopy learning materials and access to the DataMites Exclusive Learning Community further enhance the learning experience.
Last but not least, DataMites offers affordable pricing and scholarships, making quality data analytics training accessible to all.
In Itanagar, the certification in data analytics opens doors to a world of opportunities. Itanagar, being the capital city of Arunachal Pradesh, presents a unique landscape for data analytics professionals. The city's evolving business ecosystem, including sectors like tourism, agriculture, and healthcare, offers a wealth of data waiting to be analyzed. By obtaining a data analytics certification in Itanagar, individuals can position themselves for lucrative job prospects, consulting opportunities, or even entrepreneurial ventures.
Along with the data analytics courses, DataMites also provides artificial intelligence, data mining, data science, deep learning, mlops, python, AI expert, IoT, tableau, data engineer, r programming and machine learning courses in Itnagar.
Data Analytics refers to the process of collecting, organizing, analyzing, and interpreting large sets of data to uncover patterns, trends, and insights that can inform decision-making and drive business improvements. It involves applying statistical and quantitative techniques, as well as using various tools and technologies, to extract valuable information from data.
Data Analytics is widely utilized across various industries, including but not limited to:
Finance and banking
Healthcare and pharmaceuticals
Retail and e-commerce
Manufacturing and logistics
Marketing and advertising
Energy and utilities
Government and public sector
Sports and entertainment
Studying Data Analytics offers several significant benefits, including:
Improved decision-making: Data-driven insights help organizations make informed and strategic decisions based on evidence and analysis rather than intuition.
Enhanced efficiency and productivity: By analyzing data, organizations can identify areas for optimization, streamline processes, and improve operational efficiency.
Competitive advantage: Data Analytics enables businesses to gain a competitive edge by identifying market trends, customer preferences, and opportunities for innovation.
Better customer understanding: By analyzing customer data, organizations can gain insights into customer behavior, preferences, and needs, leading to personalized and targeted marketing strategies.
Career opportunities: The growing demand for data professionals makes studying Data Analytics a valuable investment, offering diverse career opportunities and growth prospects.
A career in Data Analytics is open to individuals from diverse educational backgrounds. Professionals with backgrounds in mathematics, statistics, computer science, engineering, economics, business, and other related fields can pursue a career in Data Analytics. Additionally, individuals with a passion for data analysis, problem-solving, and critical thinking can also enter this field through relevant training and upskilling.
To succeed in data analytics, it is essential to have proficiency in programming languages like Python, R, or SQL.
Strong analytical and problem-solving skills are also crucial.
Additionally, a solid understanding of statistical analysis and data visualization techniques is necessary.
Familiarity with database management systems is beneficial, and knowledge of machine learning and predictive modeling is advantageous.
The ability to work with large datasets and perform data manipulation is important.
Effective communication and storytelling skills are required to effectively convey insights to non-technical stakeholders.
Continuous learning and adaptability to evolving tools and technologies.
The scope of Data Analytics is vast and expanding rapidly. As organizations increasingly recognize the value of data-driven decision-making, the demand for skilled Data Analytics professionals continues to grow across industries. The scope extends to areas like data mining, data visualization, predictive modeling, machine learning, and artificial intelligence.
The national average salary for a Data Analyst is £36,535 per annum in the UK. (Glassdoor)
The national average salary for a Data Analyst is C$58,843 per year in Canada. (Payscale)
The national average salary for a Data Analyst is USD 69,517 per year in the United States. (Glassdoor)
The national average salary for a Data Analyst is INR 6,00,000 per year in India. (Glassdoor)
The national average salary for a Data Analyst is AUD 85,000 per year in Australia. (Glassdoor)
The national average salary for a Data Analyst is CHF 95,626 per year in Switzerland. (Glassdoor)
The national average salary for a Data Analyst is AED 106,940 per year in UAE. (Payscale)
The national average salary for a Data Analyst is ZAR 286,090 per year in South Africa. (Payscale.com)
The national average salary for a Data Analyst is SAR 95,960 per year in Saudi Arabia. (Payscale.com)
The national average salary for a Data Analyst is 46,328 EUR per annum in Germany. (Payscale)
The salary of a Data Analyst in Itanagar, like in any other location, depends on various factors such as experience, qualifications, industry, and company. According to Indeed, the average data analyst salary in Itanagar is ₹3,65,488 per annum.
Data Analytics: Data Analytics focuses on extracting insights from data to inform business decisions and solve specific problems. It involves analyzing past and current data to uncover patterns, trends, and correlations. Data Analytics typically utilizes statistical techniques, data visualization, and tools to gain actionable insights from data.
Data Science: Data Science encompasses a broader scope, combining elements of mathematics, statistics, programming, and domain knowledge. Data Scientists explore and analyze large datasets to extract insights, develop predictive models, and uncover complex patterns. Data Science involves more advanced techniques such as machine learning, deep learning, and artificial intelligence to extract valuable information and generate predictive insights.
Python: A versatile programming language with libraries such as Pandas, NumPy, and Scikit-learn for data manipulation, analysis, and machine learning.
R: A statistical programming language with extensive libraries and packages for data analysis, visualization, and statistical modeling.
SQL: A language for managing and manipulating relational databases, commonly used for data extraction, transformation, and querying.
Tableau is an advanced data visualization tool that empowers users to generate interactive and visually captivating dashboards and reports.
Power BI: A business intelligence tool by Microsoft for data visualization, reporting, and interactive data exploration.
Apache Hadoop: A framework for distributed storage and processing of large datasets, commonly used in big data analytics.
Obtain a relevant degree: Pursue a bachelor's or master's degree in a field related to data analysis, such as mathematics, statistics, computer science, economics, or business analytics. Although a degree is not always mandatory, it can provide a strong foundation for a career in data analytics.
Gain technical skills: Develop proficiency in programming languages like Python or R, as well as SQL for data manipulation and analysis. Familiarize yourself with statistical analysis, data visualization, and machine learning techniques.
Acquire practical experience: Engage in hands-on projects, internships, or freelance work to gain practical experience in data analysis. Build a portfolio showcasing your projects and demonstrate your ability to extract insights from data.
Continuous learning is crucial for staying abreast of the latest trends, tools, and techniques in data analytics. Engaging in online courses, workshops, and industry events enables you to enhance your knowledge and skills continually.
Networking is also vital in the field of data analytics. By connecting with professionals through online platforms, industry events, and professional associations, you can gain valuable insights, access job opportunities, and benefit from mentorship.
Apply for data analyst roles: Start applying for entry-level or junior data analyst positions. Highlight your skills, educational background, and practical experience in your resume and cover letter. Prepare for interviews by showcasing your problem-solving skills and ability to work with data.
The best institute for learning data analytics: DataMites is a well-known institute for learning data analytics. They offer comprehensive courses and training programs in various locations. DataMites provides in-depth knowledge and practical skills required to excel in the field of data analytics.
The best course for Data Analytics: The "Certified Data Analyst" course offered by DataMites is highly recommended for individuals aspiring to become data analysts. This course covers essential topics such as data analysis techniques, statistical analysis, data visualization, and machine learning. It equips learners with the necessary skills and knowledge to work with data and derive valuable insights.
Data Analytics offers promising career prospects, with a wide range of job opportunities available. Professionals in this field can find employment in various sectors, including technology companies, consulting firms, financial institutions, healthcare organizations, e-commerce companies, and government agencies. Data Analytics Job titles may include Data Analyst, Data Scientist, Business Intelligence Analyst, Data Engineer, Machine Learning Engineer, and Data Consultant, among others.
While a specific educational path may not be mandatory for a career in data analytics, having a degree in a related field can be advantageous. Employers often prefer candidates with a bachelor's or master's degree in mathematics, statistics, computer science, economics, business analytics, or a related discipline. Additionally, certifications and specialized training in data analytics, data science, or relevant tools can further enhance your skills and marketability.
DataMites is a reputable institute known for its quality data analytics courses. Choosing DataMites in Itanagar provides you with access to their industry-expert trainers, comprehensive course curriculum, hands-on projects, and placement assistance. We offer a flexible learning schedule, convenient location, and a supportive learning community, making it an ideal choice for data analytics training in Itanagar.
The prerequisites for attending the data analytics training in Itanagar may vary depending on the specific course. However, having a basic understanding of mathematics, statistics, and computer applications is beneficial.
There are several reasons to consider DataMites for Certified Data Analyst Training in Itanagar. Some key reasons include their experienced faculty, comprehensive course curriculum, hands-on projects with real-world data, internship opportunities, placement assistance, flexible learning options, and a supportive learning community. DataMites also provides globally recognized certifications that add value to your resume.
The DataMites Certified Data Analyst Course in Itanagar is a NO CODING COURSE open to individuals from various backgrounds, including graduates, working professionals, business analysts, IT professionals, and anyone interested in building a career in data analytics.
The fee for the Data Analytics Course in Itanagar at DataMites may vary depending on factors such as course duration, mode of delivery, and any additional services provided. The certified data analyst training fee in Itanagar can vary from INR 28,178 to INR 76,000
The duration of the DataMites Certified Data Analytics Course in Itanagar has a duration of 4 months, comprising 200+ learning hours. It is designed to provide comprehensive training and sufficient time for practical exercises and projects.
The DataMites Certified Data Analyst Training in Itanagar covers a wide range of topics, including data analysis techniques, statistical analysis, data visualization, data mining, machine learning, predictive analytics, and data-driven decision making. The detailed curriculum can be obtained from DataMites through their website or during the counseling session.
The Flexi-Pass in DataMites refers to a unique feature that allows learners to have access to the course material and resources for a duration of 365 days from the date of enrollment. This enables learners to learn at their own pace, review the content, and revisit the course material even after the completion of the training.
DataMites offers various payment methods to make it convenient for learners. The available payment methods may include online payment through debit or credit cards, net banking, UPI, or other online payment gateways. Additionally, they may also accept payment through bank transfers or demand drafts. The specific payment methods and details will be provided by DataMites during the enrollment process.
Upon successfully finishing the Data Analytics training at DataMites, you will be awarded IABAC, NASSCOM FutureSkills Prime, and JainX certifications. These globally recognized certifications from DataMites validate your proficiency and understanding in the field of data analytics. They serve as a testament to your skills and can significantly boost your career opportunities while showcasing your expertise to prospective employers.
Yes, DataMites provides support sessions to learners who require a more thorough understanding of specific topics. You can reach out to their support team or faculty to schedule additional support sessions or seek clarification on any doubts or concepts you want to explore further.
Yes, upon successfully completing the Data Analytics training from DataMites in Itanagar, you will receive data analytics course completion certification to validate your participation and completion of the training program, indicating your acquired skills and knowledge in the field of Data Analytics.
The specific documents required for the training session may vary depending on the institute's policies. However, it is advisable to carry a government-issued photo ID proof for identification purposes.
DataMites offers various payment options for enrolling in our courses. These may include online payment methods such as debit or credit cards, net banking, UPI, or other online payment gateways.
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.
IABAC Global Certifications
Comprehensive : Math, Stats, Machine Learning, Python, R, Tableau
8-month | 700 Learning Hours
Internship | Job Assistance
IABAC Global Certifications
Comprehensive: Computer vision, NLP,
Deep Learning, Reinforcement Learning
11-Month | 780 Learning Hours
Internship | Job assistance