DATA ANALYTICS CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYTICS COURSE FEE IN KOZHIKODE

Live Virtual

Instructor Led Live Online

110,000
63,945

  • 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
36,645

  • 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
69,195

  • 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 KOZHIKODE

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 KOZHIKODE

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 : OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method
     

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

OFFERED DATA ANALYTICS COURSES IN KOZHIKODE

DATA ANALYTICS TRAINING REVIEWS

ABOUT DATA ANALYTICS TRAINING IN KOZHIKODE

Data analytics has witnessed a staggering growth trajectory in recent years. According to research, the global big data analytics market size was valued at $271.83 billion in 2022 and is projected to reach $745.15 billion by 2030. (Fortune Business Insights) This exponential growth is driven by advancements in technology, the proliferation of digital platforms, and the increasing adoption of data-driven strategies by organizations worldwide. It's clear that data analytics is not just a buzzword; it's a game-changer that has the power to revolutionize industries and drive innovation on a global scale.

DataMites Institute offers an extensive Data Analytics Course in Kozhikode that equips individuals with the necessary skills and knowledge to excel in the field. The Certified Data Analyst Training program spans over 4 months, providing students with more than 200 hours of immersive learning. With a curriculum that covers statistical analysis, data visualization, machine learning, and predictive modeling, participants can expect to dedicate approximately 20 hours per week to their studies, ensuring a comprehensive understanding of the subject matter. Additionally, the inclusion of 10 Capstone Projects and 1 Client Project allows students to apply their learning to real-world situations, honing their practical data analytics skills.

There are ten compelling reasons why DataMites stands out as the preferred choice for Data Analytics Training in Kozhikode. 

  • The institute boasts a team of seasoned faculty members, including industry expert Ashok Veda, who bring a wealth of practical experience and knowledge to the program. 

  • The course curriculum is thoughtfully designed, covering a wide range of topics essential for data analysts, including statistical analysis, data visualization, machine learning, and predictive modeling. Successful completion of the program leads to globally recognized certifications, such as IABAC, NASSCOM FutureSkills Prime, and JainX, which add immense value to students' professional profiles.

  • DataMites understands the importance of flexibility in learning and offers a variety of options to suit different preferences. Whether it's the convenience of ON DEMAND data analytics offline courses in Kozhikode or the flexibility of online data analytics courses in Kozhikode, students can choose the mode that best suits their schedule and learning style. 

  • The program goes beyond theoretical knowledge, providing hands-on experience through projects using real-world data. This practical approach ensures that students develop the necessary skills to tackle complex data analytics challenges in the industry. Data Analytics Internship opportunities further enhance students' practical experience, allowing them to apply their knowledge in real-world scenarios and gain valuable industry exposure. 

  • DataMites also provides comprehensive data analytics placement assistance and job references to support students in their career journey, helping them secure promising positions in top companies.

  • In addition, students have access to hardcopy learning materials and books, enabling them to delve deeper into the subject and reinforce their understanding. 

  • Being part of the DataMites Exclusive Learning Community allows students to connect with peers, collaborate on projects, and foster a supportive network of data enthusiasts.

  • DataMites Institute prioritizes accessibility to data analytics training by providing affordable pricing options and offering scholarships to eligible students. The institute is dedicated to ensuring that individuals from various backgrounds can pursue their passion for data analytics without financial constraints.

Kozhikode presents a unique blend of a thriving business landscape, technological advancements, and a rich cultural heritage. By pursuing a data analytics certification in Kozhikode, individuals not only gain the necessary skills and expertise but also immerse themselves in a dynamic ecosystem that propels their career growth. The city's economic growth, combined with its intellectual capital and natural beauty, make it an ideal destination for aspiring data analysts to shape a successful and fulfilling future in the field of data analytics.

Along with the data analytics courses, DataMites also provides python, IoT,AI expert, data science, deep learning, mlops, data engineer, data mining, tableau, artificial intelligence, r programming and machine learning courses in Kozhikode.

ABOUT DATA ANALYTICS COURSE IN KOZHIKODE

Data Analytics refers to the process of collecting, organizing, analyzing, and interpreting large volumes of data to uncover meaningful patterns, insights, and trends that can be used for informed decision-making.

Industries such as finance, healthcare, retail, e-commerce, marketing, telecommunications, and manufacturing make extensive use of Data Analytics to gain insights, improve operations, and make data-driven decisions.

The scope of Data Analytics is vast and expanding. With the increasing availability of data and advancements in technology, there is a growing demand for professionals who can extract valuable insights from data and drive business growth.

The field of Data Analytics offers a wide range of career prospects. Roles such as Data Analyst, Data Scientist, Business Analyst, Data Engineer, and Data Architect are in high demand across various industries. These roles offer opportunities for growth, specialization, and leadership positions.

  • 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 ZAR 286,090 per year in South Africa. (Payscale.com)

  • 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 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 average global salary of a Data Analyst varies depending on factors such as location, experience, industry, and skills. On average, a Data Analyst in Kozhikode can earn around ₹2,71,967 per year. (Indeed)

DataMites is widely recognized as a top institute for data analytics training. They provide extensive courses and training programs across multiple locations, equipping learners with comprehensive knowledge and practical skills essential for success in the field of data analytics.

For individuals aspiring to pursue a career in data analytics, the "Certified Data Analyst" course provided by DataMites is an excellent choice. This comprehensive course focuses on crucial aspects like data analysis techniques, statistical analysis, data visualization, and machine learning. By enrolling in this program, learners acquire the essential skills and knowledge required to effectively handle data and extract valuable insights.

The cost of a Data Analytics Course may differ based on factors such as the institute you choose, the duration of the course, the curriculum, and any additional features provided. Typically, the price range for data analytics training in Kozhikode is between 40,000 and 80,000 INR.

While coding skills are beneficial in the field of Data Analytics, they are not always mandatory. Proficiency in programming languages like Python, R, SQL, or tools like Excel and Tableau can enhance a data analyst's capabilities and job prospects. However, the level of coding required may vary depending on the specific job role and industry.

The monthly salary of an entry-level Data Analyst in India can vary based on factors such as location, company size, industry, and skills. According to Ambitionbox, the average annual starting salary for a Data Analyst in India is approximately ₹1.6 Lakhs, which translates to around ₹13.3k per month.

Being a data analyst can be considered a challenging job as it requires a combination of analytical skills, problem-solving abilities, domain knowledge, and proficiency in data analysis techniques and tools. However, with the right training, continuous learning, and practical experience, one can overcome these challenges and excel in the field.

Data Analytics can be a good career option for freshers as it offers promising job prospects, competitive salaries, and opportunities for growth. With the increasing reliance on data-driven decision-making in various industries, the demand for skilled data analysts is expected to continue growing.

Graduation is not always a mandatory requirement for becoming a data analyst. However, having a bachelor's degree in fields such as computer science, statistics, mathematics, engineering, or business can be advantageous and increase job opportunities. Additionally, relevant certifications, practical experience, and strong analytical skills are also highly valued in the field of Data Analytics.

While it may be challenging to land a data analyst job without any experience, it is not entirely impossible. Entry-level positions or internships may be available for individuals who possess relevant educational qualifications, certifications, and a strong understanding of data analytics concepts. Additionally, showcasing practical projects, participating in online competitions, and continuously developing your skills can improve your chances of getting hired as a data analyst with little or no experience.

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FAQ’S OF DATA ANALYTICS TRAINING IN KOZHIKODE

DataMites is the preferred choice for Data Analytics Courses in Kozhikode due to its comprehensive and industry-relevant curriculum, experienced trainers, and practical learning approach. They offer flexible training options, including classroom and online modes, to cater to individual preferences and provide hands-on experience with real-world projects.

DataMites offers Certified Data Analyst Training in Kozhikode with a focus on practical application and industry-oriented skills. Their trainers are seasoned professionals with vast experience, ensuring high-quality learning. Additionally, they provide globally recognized certifications upon successful completion, boosting career prospects.

The prerequisites for data analytics training at DataMites in Kozhikode may vary based on the specific course. However, basic knowledge of mathematics, statistics, and computer usage is usually beneficial.

Aspiring data analysts, professionals seeking to upskill in data analytics, graduates, and anyone with an interest in data analysis can enroll in the DataMites Certified Data Analyst Course in Kozhikode.

The price range for the Data Analytics Course in Kozhikode at DataMites is flexible and dependent on various factors, including the duration of the course, the method of delivery, and any supplementary offerings. Generally, the fee for the certified data analyst training in Kozhikode can vary between INR 28,178 and INR 76,000, offering different options to suit individual preferences and requirements.

The DataMites Certified Data Analytics Course in Kozhikode is structured to span over a period of 4 months, encompassing more than 200 hours of learning. This well-designed course aims to offer comprehensive training, allowing ample time for hands-on practical exercises and projects, ensuring learners acquire practical skills and valuable experience in the field of data analytics.

The DataMites Certified Data Analyst Training in Kozhikode covers a wide range of topics, including data analysis techniques, statistical analysis, data visualization, machine learning, and more.

The Flexi-Pass offered by DataMites allows learners to access multiple courses at a discounted price. It gives flexibility in choosing and attending different courses as per individual learning needs and preferences.

After successfully completing the Data Analytics training at DataMites, you will receive prestigious certifications from IABAC, NASSCOM FutureSkills Prime, and JainX. These certifications are globally acknowledged and serve as a testament to your expertise and proficiency in data analytics. They can significantly enhance your career prospects and demonstrate your skills to potential employers.

DataMites has a team of experienced trainers who specialize in data analytics. These trainers have industry experience and expertise in the field of data analytics.

DataMites provides various training options for data analytics, including classroom training, online training, corporate training, and self-paced learning. These options cater to different learning preferences and requirements.

DataMites may offer trial classes or demo sessions for prospective learners to experience their teaching methodology and course content.

DataMites offers classroom training for data analytics in Kozhikode based on demand. They organize interactive and instructor-led sessions in a traditional classroom environment, allowing learners to engage actively and benefit from the expertise of the instructors. This approach ensures effective learning and enables participants to apply the concepts practically in real-time scenarios.

DataMites accepts various payment methods, including online payment gateways, bank transfers, and other convenient modes of payment. It is best to inquire with them for specific details.

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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