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
With a carefully thought-out programme that demonstrates 360-degree training, DataMites™ is the world's leading institute for data analytics courses. DataMites™ offers a variety of flexible learning options, including live online classes, excellently recorded sessions, and classroom teaching. IABAC, a European Union framework has recognised our data analytics certification programme in Ahmedabad. DataMites can help you obtain a top-notch training environment at a reasonable price.
The four-month-long DataMites Data Analytics Courses in Ahmedabad are offered in both online data analytics training and data analytics classroom training formats, and they are taught using a three-phase teaching methodology.
Phase 1: Candidates will be given top-notch materials for self-study to help them complete the entire curriculum and prepare for the upcoming course.
Phase 2 is the primary portion of the Live Intensive Training and it consists of the IABAC Data Analytics Certification, which is an international certification, as well as practical capstone projects.
Projects, internships, and a programme to help participants get ready for the workforce to make up Phase 3.
The data analytics industry has seen tremendous growth over the past ten years as a result of India's explosive growth in internet users. The World Bank estimates that by 2025, there will be more than 900 million additional internet users, with internet penetration rising from 20% in 2018 to 41% in 2019. The amount of data that can be used to tap into other businesses has increased dramatically as a result of this expansion, which has benefited the data analytics industry. Candidates learn everything from scratch because our Data Analytics Courses in Ahmedabad are comprehensive in and of themselves. The Excel, Advanced Excel, Tableau, SQL, Power BI, Basics of R, and Python technologies are covered in our data analytics course. We provide you with specialised training, giving you the knowledge and abilities you need to take a step closer to landing your ideal career. The data analytics course fee in Ahmedabad is 42,000 INR.
Ahmedabad, the most populous city in the Indian state of Gujarat has become a megacity for international business and Information Technology. Ahmedabad has turned into a prominent location for the IT world and has given birth to booming industries in the field of Data Analytics and Data Science.
If you want to work in the data analytics domain, you may have considered whether you should obtain the best certification in data analytics or simply enhance your resume with relevant data abilities. Numerous data analytics programmes in Ahmedabad are available, and each one has a financial stake in convincing you that you need it.
DataMites Certified Data Analyst Course in Ahmedabad is your way to go. The Certified Data Analyst Course Fee in Ahmedabad was previously 55,000 INR with certificates from IABAC & JainX but has since been reduced by 20% to just 44,900 INR.
There are lots of opportunities for careers in data analytics, and salaries are competitive. Data analytics can be applied across all business levels and across a variety of industries. As a result, setting pay and growth objectives may be challenging. According to Payscale, a data analyst's average salary in Ahmedabad is 4,30,000 and glassdoor revealed that a data analyst in Ahmedabad earns a moderate amount of 3,12,706 LPA!
In 2021, the data analytics sector had a significant 26.5 percent year-over-year increase, with a market value of US$ 45.4 billion. Moreover, LinkedIn and the US Bureau of Labor had confirmed that the role of Data Analyst would be the most in-demand by 2022.
Along with the data analytics courses, DataMites also provides artificial intelligence, data engineer, data science, deep learning, tableau, python training, r programming, and machine learning courses in Ahmedabad.
So why are you still waiting? Participate in Data Analytics Training in Ahmedabad. Grab the opportunity!
Data analytics is the practice of analysing datasets to make inferences about the information they have. Using data analytics approaches, you can take unstructured data and find patterns to draw out insightful conclusions.
Anyone interested in learning more about Data Analytics and Data Science is welcome to enrol in the course. The minimum requirement for a postgraduate Data Analytics Programme in Ahmedabad is a Bachelor's degree from a recognised university with at least 50% overall or the equivalent, ideally in the fields of science or computer science.
Top firms today place a high priority on data analytics.
Expanding employment possibilities
Professionals in data analytics are earning more money.
There is big data analytics everywhere.
You will have a variety of job titles to pick from and be in charge of all corporate decisions.
One of the most sought-after careers in 2022 is data analysis. India is the second major centre for data-related employment after the United States. Depending on the training level you want, there will be a difference in cost. The cost of data analytics training might be anything between 30,000 and 100,000 Indian rupees.
While a degree isn't necessarily necessary for a data analyst position, getting the necessary certification from a reputable organisation is essential. The skills required for success in data analytics can be learned in anywhere between six weeks and two years. 4 months of training can be a great method to master data analytics and become well-versed. The vast variation is explained by the fact that there are numerous unique job roles in data analytics.
Every industry uses data analysts, and they have a variety of job titles. Retail, healthcare, banking & finance, transportation, education, construction, and technology are examples of typical sectors. You can work in the fields of data analytics, data science, business intelligence analysis, data engineering, quantitative analysis, data consulting, operations analysis, marketing analysis, project management, information technology systems analysis, and transportation logistics, to mention a few.
The essential function of modern firms is data analysis. Since no single data analytics tool can meet all needs, selecting the best one might be difficult. Some of the essential instruments used for data analytics are Excel, Advanced Excel, Tableau, SQL, Power BI, Basics of R, and Python.
Technical skills like data analysis, statistical knowledge, data storytelling, communication, and problem-solving would be advantageous for learning Data Analytics. Business intuition and strategic thinking are also considered important for data analysts that often partner with business stakeholders.
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 in the UK is £36,535 per annum. (Glassdoor)
The national average salary for a Data Analyst in India is INR 6,00,000 per year. (Glassdoor)
The national average salary for a Data Analyst in Australia is AUD 85,000 per year. (Glassdoor)
The national average salary for a Data Analyst in Germany is 46,328 EUR per annum. (Payscale)
The national average salary for a Data Analyst in Switzerland is CHF 95,626 per year. (Glassdoor)
The national average salary for a Data Analyst in Canada is C$58,843 per year. (Payscale)
The national average salary for a Data Analyst in UAE is AED 106,940 per year. (Payscale)
The national average salary for a Data Analyst in Saudi Arabia is SAR 95,960 per year. (Payscale.com)
The national average salary for a Data Analyst in South Africa is ZAR 286,090 per year. (Payscale.com)
Some of the most sought-after specialists worldwide are skilled data analysts. Data analysts command high pay and top benefits, even at the entry-level, due to the high demand for their services and the scarcity of qualified candidates. According to Payscale, a data analyst's average salary in Ahmedabad is 4,30,000 and glassdoor revealed that a data analyst in Ahmedabad earns an average amount of 3,12,706 LPA!
The ideal college for you, if you want to pursue a career in analytics, is DataMites. The primary mentors are knowledgeable professionals who are industry-oriented, and the course curriculum is well-planned. We provide projects and internship possibilities for practical experience! The finest educational setting for you if you want to work in the analytics sector is DataMites. The primary mentors are knowledgeable and dedicated to the profession, and the course material is well-developed. Projects and internship possibilities are available for professional skills!
The phrase "data analytics" has become more popular today due to the rise in data generation. As the curriculum is designed to train applicants from level 1, there are no formal prerequisites for the DataMites Data Analytics Course. However, having prior understanding of programming languages, databases, data structures, mathematics, and algorithms will only be advantageous.
The Certified Data Analyst Course, which validates your ability to confidently evaluate data using a variety of technologies, is the highest accreditation in data analytics. The ability to handle data, perform exploratory research, understand the fundamentals of analytics, and visualise, present, and elaborate on your results are all skills that are demonstrated by certification. The respected Jain University and IABAC also accept the DataMites CDA Course.
Your greatest option in the field is the DataMites data analyst certification course in Ahmedabad. Our data analytics course provides you with tangible proof that you are qualified to help companies, including well-known multinationals, interpret the data at hand. It is evident that you are qualified to carry out the responsibilities of a particular employment role in accordance with industry standards, as opposed to a data analytics certificate.
Both freshmen and undergraduate students may enrol in the course. Following a profession as a data analyst will be the best choice for you if you want to go from an IT profile to a business profile. You will have a decent chance of succeeding in this sector if you have any potential for coding and IT skills. DataMites Data Analytics Courses in Ahmedabad are also open to non-IT professionals working in industries like human resources, banking, marketing, and sales, among others.
For roles as a data scientist and analyst, organisations do indeed hire recent graduates. In the majority of cases, post-graduation or expertise are not required for entry-level analytics positions. In some firms, an engineering degree is the only requirement, and it doesn't even matter what stream it is from. Your aptitude, communication abilities, and critical thinking are the only things these companies are interested in.
The International Association of Business Analytics Certifications has granted DataMitesTM its accreditation as a global institute for data science (IABAC).
We have roughly trained 50,000+ candidates.
The three-phase learning process was painstakingly created to deliver the greatest instruction possible.
Participate in practical projects and really useful case studies.
Obtain the worldwide IABAC and JainX Data Analytics Certification.
Help with internships and employment
The cost of Data Analytics Training in Ahmedabad at DataMites will be around 42,000 INR.
In a data-driven environment, there are several benefits to completing data analytics training and being a certified data analytics specialist. You will receive 4 months of data analytics training at DataMites.
You should definitely finish the DataMites Certified Data Analyst Training in Ahmedabad if you're thinking about working as a data analyst. Our programme promises to offer the knowledge, assurance, and credentials necessary to start a career as a data analyst from scratch.
One of the top data analytics programmes offered by DataMites is the Certified Data Analyst curriculum, which has been accredited by the IABAC and Jain University international recognised agencies, whose credentials you would obtain after completing the course. The best way to start a career in data analytics is to obtain the DataMites Certified Data Analyst Course in Ahmedabad.
You have a variety of learning options with DataMites, including data analytics training online in Ahmedabad, self-study courses, and classroom training in data analytics. Every training session is designed to help participants flourish in the field.
Data analytics has grown to be a large field, so we want to develop skilled workers in the domain. Our instructors at DataMites are highly knowledgeable and have hands-on experience in the data field, so they can provide the finest learning environment for your upcoming big move.
Candidates may attend sessions from Datamites for a period of three months pertaining to any query or revision you wish to clear with our Flexi-Pass for Data Analytics Certification Training in Ahmedabad.
Your IABAC® certification and JainX certification, which provide global recognition of the essential abilities and pave the road for your future employment in the industry, will be given to you once you have been accredited by IABAC and Jain University.
Without a doubt, we'll give you a certificate of completion for your data analytics course in Ahmedabad after it's through.
Yes, we do have demo sessions that are held for free and give prospective students a general summary of what the upcoming course would entail. You are welcome to attend these sessions to gain an idea of what the training will include before deciding whether to continue.
You shouldn't be bothered about that. Simply get in touch with your trainers about it and arrange a class that works with your schedule. Every session of the online data analytics courses in Ahmedabad will be recorded and uploaded, allowing you to quickly catch up on anything you missed at your own pace and comfort. Learning data analytics has never been simpler, in fact!
Yes, after the course is over, DataMites has a specialised Placement Assistance Team (PAT) that will help you find a job and prepare you for interviews.
We take payments using; (Ahmedabad)
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