DATA ANALYTICS CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYTICS COURSE FEE IN LUDHIANA

Live Virtual

Instructor Led Live Online

110,000
59,378

  • IABAC® & JAINx® Certification
  • 6-Month | 200+ Learning Hours
  • 20 HOURS LEARNING A WEEK
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

55,000
34,028

  • Self Learning + Live Mentoring
  • IABAC® & JAINx® Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Classroom

In - Person Classroom Training

110,000
64,253

  • IABAC® & JAINx® Certification
  • 6-Month | 200+ Learning Hours
  • 20 HOURS LEARNING A WEEK
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship +Job Assistance

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UPCOMING DATA ANALYTICS ONLINE CLASSES IN LUDHIANA

BEST DATA ANALYTICS CERTIFICATIONS

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.

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WHY DATAMITES INSTITUTE FOR DATA ANALYTICS COURSE

Why DataMites Infographic

SYLLABUS OF DATA ANALYTICS CERTIFICATION IN LUDHIANA

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
• Modeling
• Evaluation
• Deploying
• Monitoring

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
• 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 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
• Challenges
• 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
• 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: 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: 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: 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

OFFERED DATA ANALYTICS COURSES IN LUDHIANA

DATA ANALYTICS TRAINING REVIEWS

ABOUT DATA ANALYTICS TRAINING IN LUDHIANA

DataMites is one of the premier training providers in India due to its strong market position and five years of instructing roughly 50,000 students. Only those who are eager to build a solid foundation in the data-driven industry and data analytics beginners and seasoned professionals should enrol in the Data Analytics Course in Ludhiana, which employs a hands-on learning methodology. We provide Data Analytics Training in Ludhiana with placement in order to primarily equip people for professions in the field. Following the conclusion of their projects and internships, students have more than enough experience to be considered for a job offer with a six-month duration. In Ludhiana, the cost of the data analytics course is 42,000 INR.

The following three detailed steps make up our learning strategy:

Phase 1 of data analytics training is the period before the training really begins. For the solitary study, there are currently materials and study aids available.

The first elements of Phase 2 of the curriculum for upcoming on-the-job training are capstone projects and online data analytics training. A data analytics certificate from IABAC is also provided!

Phase 3 distinguishes itself from Phase 2 and ensures that the candidates have a thorough comprehension of the subject matter through projects, internships, and the Job Ready Program for the applicants, to name just a few!

Combining the DataMites Certified Data Analyst Course in Ludhiana with our dedicated career advice can help you find your next data-related job. By assisting you in obtaining professional certification, DataMites will help you begin your new job in the data industry. The cost of our certified data analyst course in Ludhiana is 55,000 INR, however, right now it's only 44,900 INR. It has IABAC and JainX certifications.

On the banks of the Sutlej River lies Ludhiana, a big industrial city in Punjab. Ludhiana dubbed the "Manchester of India," is an important manufacturing area in India. It is absurd to discover such a highly regarded education that is within your means in the extremely lucrative field of data analytics and data science.

A data analyst certification in Ludhiana verifies your skill and understanding in a particular field of big data analysis. Finding a traditionally-trained data analyst who can use the various corporate data analytics tools and processes that are being developed on a daily basis is nearly difficult, even for the biggest corporations. The average pay for a data analyst in India is 4,64,926 INR according to Payscale. In Ludhiana, a data analyst makes an average salary of 3,07,180 LPA! (Indeed) Furthermore, by 2022, the position of Data Analyst would be the most in-demand, according to LinkedIn and the US Bureau of Labor.

Aside from the high demand and corresponding income, Data Analysts have the ability to collaborate and contribute to the highest levels of decision-making, which can lead to opportunities for advancement into more management roles. So why wait? Do yourself a favour and enrol in our accredited Data Analytics Training in Ludhiana!

Along with the data analytics courses, DataMites also provides python training, data engineer, machine learning, deep learning, tableau, artificial intelligence, r programming, and data science courses in Ludhiana.

ABOUT DATA ANALYTICS COURSE IN LUDHIANA

A discipline called data analytics is dedicated to drawing conclusions from data. It includes the procedures, equipment, and methods for gathering, organising, and storing data as well as data analysis and management. Applying statistical analysis and technology to data in order to identify trends and resolve issues is the main goal of data analytics.

In the fields of engineering, computer science, and management, there are postgraduate courses in data science accessible. A Bachelor's degree with at least 50% overall or an equivalent grade from a reputable university, ideally in the fields of science or computer science, is the minimum requirement for enrollment in a data analytics course.

A very solid career in data analytics. There has never been a better time to be a data professional, to put it simply. Data creation is increasing at a rate of 2.5 quintillion bytes each day. 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.

A degree is typically not required for a position as a data analyst, but getting the right certification from an accredited college is crucial. It could take anywhere from six weeks to two years to learn the skills needed for success in data analytics. A 4-month training programme is an efficient way to learn about and master data analytics. Because there are so many different paths one might take to become a data analytics specialist, the variability is explained by this.

The benefits of a career in data analytics won't manifest without extensive training and effort. Data analysts require a particular set of skills to succeed in their line of work, and while having a technical background is important, they also need a few soft skills.

Data Clearing and Information Display

Linear algebra, MATLAB, R, Python, and NoSQL Machine Learning Calculus

Communication and Critical Thinking in Excel for Windows

  • 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 £36,535 per annum in the UK.  (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 C$58,843 per year in Canada. (Payscale)

  • 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 46,328 EUR per annum in Germany. (Payscale)

  • 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 SAR 95,960 per year in Saudi Arabia. (Payscale.com)

  • The national average salary for a Data Analyst is ZAR 286,090 per year in South Africa. (Payscale.com)

Outside of the computer and digital industries, data science offers career opportunities and transferable skills.

  • Business Intelligence Analyst

  • IT Systems Analyst

  • Data Analyst Consultant

  • Data Analyst

  • Data Scientist

  • Data Engineer

  • Quantitative Analyst

  • Marketing Analyst

  • Project Manager

  • Operations Analyst

Among the most in-demand specialists worldwide are skilled data analysts. Data analysts earn enormous incomes and top-notch benefits, even at the entry level, due to the high demand for their services and the relatively small pool of qualified candidates. The average pay for a data analyst in India is 4,64,926 INR according to Payscale. In Ludhiana, a data analyst makes an average salary of 3,07,180 LPA! (Indeed)

Since there is a growing need for data specialists but a limited supply, there are many excellent work prospects in this industry. If you want to pursue a career in the analytics industry, DataMites is the finest institute for you. The primary mentors are knowledgeable and industry-oriented, and the course curriculum is skillfully laid out. With projects and internship opportunities, we provide practical training!

The phrase "data analytics" has gained popularity in recent years due to the rise in data generation. The DataMites Data Analytics Course has no formal prerequisites because it is designed to train candidates starting at level 1. However, having a prior understanding of programming languages, databases, data structures, mathematics, and algorithms will only be favourable.

The ultimate accreditation in data analytics is the Certified Data Analyst designation, which attests to your competence in confidently evaluating data utilising a range of technologies. Your proficiency in manipulating data, conducting exploratory research, comprehending the fundamentals of analytics, and visualising, presenting, and expanding on your findings is demonstrated by your certification. The DataMites Certified Data Analyst Course in Ludhiana is recognised by both IABAC and the prestigious Jain University.

Your highest bet in the field is the DataMites certified data analyst course in Ludhiana. Our data analytics training provides you with tangible proof that you are qualified to help businesses, including well-known multinationals, interpret the data at hand. It is evidence that you are qualified to carry out the responsibilities of a certain job role in accordance with industry standards, as opposed to a data analytics certificate.

Furthermore, highly developed coding abilities are not necessary for data analysts. Instead, they should have knowledge of data management applications, data visualisation applications, and analytics applications. Data analysts, like most people in the data industry, need to have strong mathematical abilities.

The future is in data analysis. Better data collection, processing, and interpretation will lead to increased productivity. One of the most sought-after professions in the world today is skilled data analyst. Data Analysts earn enormous incomes and fantastic benefits, even at the entry level, due to the high demand for their services and the small pool of qualified candidates.

Both business analysts and data analysts support the use of data in their firms' decision-making processes. While business analysts are typically more active in addressing company problems and suggesting solutions, data analysts typically work more directly with the data itself. These two positions are both in high demand and frequently pay well.

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FAQ’S OF DATA ANALYTICS TRAINING IN LUDHIANA

Both amateurs and experts are welcome to enrol in the training. The best option for you if you want to switch from an IT to a business profile is a profession as a data analyst. You will be well-suited to succeed in this industry if you are skilled in coding and IT. Anyone is welcome to enrol in DataMites Data Analytics Training in Ludhiana, including those who perform in the banking, human resources, marketing, or sales sectors.

DataMitesTM is a global institute for data science that has received approval from the International Association of Business Analytics Certifications (IABAC).

  • More than 50,000 candidates were trained

  • The three-phase learning technique was painstakingly constructed to deliver the best training possible.

  • Participate in worthwhile initiatives and case studies.

  • Get the JainX Data Analytics Certification and the global IABAC certification.

  • Assistance in finding internships and jobs

For a data analyst with the required level of experience on your part and the right training in data analytics, the sky is the limit. Courses in data analytics are available at DataMites for four months.

One of DataMites' top data analytics programmes, the Certified Data Analyst curriculum, has been acknowledged by the illustrious organisations IABAC and JainX, whose credentials you would earn upon successfully completing the programme. Earning the DataMites Certified Data Analyst certification is encouraged to launch a data analytics career.

Data analytics has become a broad topic, thus we wish to train informed experts in it. DataMites' instructors are very knowledgeable and have practical expertise in the data industry, so they can offer the best learning environment for your future significant step.

For a period of three months, participants in our Flexi-Pass for Data Analytics Certification Training are allowed to attend sessions led by Datamites that are pertinent to any question or revision they wish to pass.

Online data analytics training in Ludhiana from DataMites is now as powerful as traditional classroom instruction. Students benefit from the assignment by learning the topic as well as by developing better time management skills, which enhances communication and increases the amount of feedback you receive from your instructor. In many cases, online education is less expensive than traditional classroom instruction.

Carry your valid Photo documents, such as a passport or driver's licence, when you register for the certification exams and receive your participation certificate.

Three stages of learning are offered by DataMites. For self-study in Phase 1, candidates will be given books and videos to assist them learn everything they need to know about the programme. The first part of the intensive live online training is Phase 2, and after you complete it, you'll obtain the IABAC Data Analytics Certification, a universal credential. And we'll assign projects and placements in the third phase.

You'll surely receive a certificate of completion for your data analytics course in Ludhiana when it's all said and done.

Because novices to the area are given a thorough explanation of the entire subject, the DataMites Data Analytics Training is painstakingly planned and organised in this manner. If learning analytics appeals to you, you can enlist without a second glance.

Yes, after the course is finished, our dedicated Placement Assistance Team (PAT) at DataMites will offer you placement services, including help finding a job and interview preparation.

We take payments using; 

  • Cash

  • Credit Card

  • PayPal

  • Visa

  • Master Card

  • American Express

  • Net Banking

  • Cheque

  • Debit Card

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: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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