DATA ANALYST CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYST LEAD MENTORS

DATA ANALYST COURSE FEE IN KENYA

Live Virtual

Instructor Led Live Online

KES 157,140
KES 89,174

  • IABAC® 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

KES 78,570
KES 51,105

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

Corporate Training

Customize Your Training


  • Instructor-Led & Self-Paced training
  • Customized Learning Options
  • Industry Expert Trainers
  • Case Study Approach
  • Enterprise Grade Learning
  • 24*7 Cloud Lab

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UPCOMING DATA ANALYST ONLINE CLASSES IN KENYA

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 ANALYST COURSE

Why DataMites Infographic

SYLLABUS OF DATA ANALYST COURSE IN KENYA

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 Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators

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
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions

MODULE 1 : OVERVIEW OF STATISTICS 

  • Introduction to 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
  • Types of Sampling
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Multi stage Sampling
  • 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 & Properties
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance
  • Covariance & Correlation

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Critical Region
  • Types of Hypothesis Testing
  • Hypothesis Testing Errors : Type I And Type Ii
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test
  • Application of Hypothesis testing

MODULE 1: COMPARISION AND CORRELATION ANALYSIS

• Data comparison Introduction,
• Performing Comparison Analysis on Data
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Hands-on case study : Comparison Analysis
• Hands-on case study Correlation Analysis

MODULE 2: VARIANCE AND FREQUENCY ANALYSIS

• Variance Analysis Introduction
• Data Preparation for Variance Analysis
• Performing Variance and Frequency Analysis
• Business use cases for Variance Analysis
• Business use cases for 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: Manufacturing

MODULE 5: PARETO (80/20 RULE) ANALSYSIS

• Pareto rule Introduction
• Preparation Data for Pareto Analysis,
• Performing Pareto Analysis on Data
• Insights on Optimizing Operations with Pareto Analysis
• 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

MODULE 7: DATA ANALYSIS BUSINESS REPORTING

• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements

MODULE 1: DATA ANALYTICS FOUNDATION

• Business Analytics Overview
• Application of Business Analytics
• Benefits of Business Analytics
• Challenges
• Data Sources
• Data Reliability and Validity

MODULE 2: OPTIMIZATION MODELS

• Predictive Analytics with Low Uncertainty;Case Study
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity

MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION

• Mathematics behind Linear Regression
• Case Study : Sales Promotion Decision with Regression Analysis
• Hands on Regression Modeling in Excel

MODULE 4: DECISION MODELING

• Predictive Analytics with High Uncertainty
• Case Study-Monte Carlo Simulation
• Comparing Decisions in Uncertain Settings
• Trees for Decision Modeling
• Case Study : Supplier Decision Modeling - Kickathlon Sports Retailer

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;
• Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool

MODULE 4: ML ALGO: KNN

• Introduction to KNN; Nearest Neighbor
• Regression with KNN
• Hands-on: KNN with ML Tool

MODULE 5: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means Clustering

MODULE 6: ML ALGO: DECISION TREE

• Decision Tree and How it works
• 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
• Hands-on: SVM with ML Tool

MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)

• Introduction to ANN, How It Works
• Back propagation, Gradient Descent
• Hands-on: ANN with ML Tool

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

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
• Self Join, Cross join
• Windows Functions: Over, Partition, Rank

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

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

MODULE 1: TABLEAU FUNDAMENTALS

• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies

MODULE 2: POWER-BI BASICS

• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION

MODULE 3: DATA TRANSFORMATION TECHNIQUES

• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values

MODULE 4: CONNECTING TO VARIOUS DATA SOURCES

• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model

OFFERED DATA ANALYST COURSES IN KENYA

DATA ANALYST COURSE REVIEWS

ABOUT DATA ANALYST TRAINING IN KENYA

The global big data analytics market soared to a staggering valuation of 240 billion U.S. dollars in 2021. Poised for significant growth, projections suggest a remarkable surge, surpassing 650 billion dollars by 2029, as reported by Statista. Kenya stands at the forefront of harnessing the power of data analytics. The nation's commitment to technological advancement is shaping a dynamic landscape for professionals in the Data Analytics domain, contributing to innovation and informed decision-making across sectors.

DataMites stands as a premier institute for Data Analytics, globally recognized for offering the Certified Data Analyst Course in Kenya. Tailored for beginners and intermediate learners, this data analytics career-oriented program imparts a robust foundation in Data Analysis, Data Science Foundation, Statistics, Visual Analytics, Data Modeling, and Predictive Modeling. With a commitment to excellence, our courses also include the esteemed IABAC Certification, ensuring a comprehensive and industry-relevant learning experience.

DataMites Structured Learning Phases:

At DataMites, our certified data analyst training in Kenya unfolds in three comprehensive phases, ensuring a systematic and effective learning journey for individuals in Kenya.

Phase 1: Pre-Course Self-Study

Embark on your educational voyage with pre-course self-study, featuring high-quality videos designed for easy comprehension. Lay the groundwork for subsequent modules through this preliminary phase.

Phase 2: 3-Month Live Training

Engage in an intensive 3-month live training phase, committing 20 hours per week. Benefit from a comprehensive syllabus, hands-on projects, and guidance from expert trainers and mentors, fostering a holistic learning experience.

Phase 3: 3-Month Project Mentoring

Conclude your training with a 3-month project mentoring phase, participating in 5+ capstone projects. This phase includes real-time internship exposure, a client/live project, and certifications from IABAC and data analytics internship, solidifying your readiness for the professional realm.

Reason to Choose Certified Data Analyst Courses in Kenya from DataMites

Ashok Veda and Academic Excellence:

At DataMites, we take pride in our distinguished faculty, led by Ashok Veda, a stalwart with over 19 years of experience in Data Analytics and AI. As the Founder & CEO at Rubixe™, Ashok Veda exemplifies expertise, ensuring top-tier education for aspiring professionals in Kenya.

Comprehensive Course Curriculum:

Our Data Analytics Training in Kenya, a 6-month endeavor, features a no-code program with an optional Python track. Dive into 20 hours of learning each week, accumulating over 200 learning hours, and earn the prestigious IABAC® Certification, a globally recognized credential.

Practical Learning and Internship Opportunities:

Immerse yourself in real-world projects, encompassing 5+ capstone projects and a client/live project, providing hands-on experience. Avail yourself of data analytics internship opportunities, setting the stage for a practical understanding of the field.

Career Guidance and Supportive Community:

Experience end-to-end job support, personalized resume crafting, data analytics interview preparation, and job updates. Join our exclusive learning community at DataMites, fostering a collaborative environment for continuous growth.

Affordable Pricing and Scholarships:

Embrace affordable pricing, with Data Analytics course fees in Kenya ranging from KES 68128 to KES 209491. Explore scholarship opportunities, making quality education accessible to a wider audience.

Kenya's Data Analytics industry is undergoing significant growth, aligning with global trends. The nation's increasing adoption of data-driven strategies across sectors positions it as a hub for professionals in the field. In Kenya, Data Analysts are highly valued, commanding an average annual data analyst salary in Kenya of KES 864,168, as reported by Payscale. With the demand for skilled Data Analysts on the rise, individuals in Kenya enjoy lucrative opportunities, solidifying their status as highly paid contributors to the thriving Data Analytics landscape.

Embark on a transformative career journey in Kenya with DataMites. Beyond our stellar Data Analytics Training Courses in Kenya, explore a spectrum of courses in Artificial Intelligence, Machine Learning, Data Engineering, Python, Tableau, Data Science, and more. Our commitment to quality education, real-world projects, and career support positions DataMites as the definitive choice for those aspiring to carve a successful niche in the evolving tech landscape of Kenya.

ABOUT DATAMITES DATA ANALYST COURSE IN KENYA

The future of data analysis holds great promise, fueled by advancements in artificial intelligence, machine learning, and big data technologies. The increasing reliance on data-driven decision-making across various industries is poised to create ample opportunities for skilled professionals.

Data analytics involves systematically examining raw data to reveal meaningful insights, patterns, and trends. Employing statistical and computational techniques, it transforms data into valuable information for informed decision-making across diverse industries.

Practical applications of data analytics encompass detecting fraud in finance, implementing personalized marketing in e-commerce, executing predictive maintenance in manufacturing, analyzing healthcare data for improved patient outcomes, and optimizing supply chain logistics through data-driven insights.

Critical skills for data analytics include proficiency in programming languages like Python or R, statistical analysis, data visualization, database management, and critical thinking. Effective communication is also vital for conveying insights to non-technical stakeholders.

Key roles in data analytics include Data Analyst, Business Intelligence Analyst, Data Scientist, Database Administrator, and Machine Learning Engineer. Each position contributes uniquely to leveraging data for organizational decision-making.

To embark on a career as a Data Analyst, individuals should seek relevant education, often a bachelor's degree, develop proficiency in data analysis tools and programming languages, gain practical experience through internships or projects, and consistently update skills to stay abreast of industry trends.

Gaining proficiency in data analytics requires mastery of key tools. Proficiency in programming languages like Python or R facilitates data manipulation, while expertise in SQL is vital for effective database management. Additionally, being familiar with data visualization tools such as Tableau or Power BI enhances the presentation of insights, creating a comprehensive and hands-on learning experience.

Undoubtedly, the study of data analytics is challenging, demanding expertise in statistics, programming, and a nuanced understanding of business dynamics. Navigating through diverse datasets to extract meaningful insights requires a robust skill set, making the course intellectually stimulating and rewarding.

While achieving complete mastery in data analytics within six months is an ambitious goal, focused dedication, hands-on projects, and targeted learning can lead to a solid foundation and practical skills. The timeframe, however, depends on individual commitment and prior knowledge.

In the cybersecurity domain, data analytics serves as a crucial tool for detecting and preventing threats by analyzing extensive datasets. It enables the identification of anomalies and patterns, allowing cybersecurity professionals to proactively strengthen digital defenses against potential breaches.

Data analytics plays a transformative role in education by offering insights into student performance, learning patterns, and predictive outcomes. Educators utilize this information to tailor learning experiences, improve engagement, and optimize teaching methods, thereby creating a more effective and personalized educational environment.

To embark on a journey of learning data analytics, start by enrolling in reputable courses or pursuing degrees in the field, available on online platforms. Engage in practical projects, collaborate with online communities, and stay abreast of industry trends to solidify your skills. Continuous learning and hands-on experience are key to mastering data analytics.

If you're new to Data Analytics, consider projects like analyzing e-commerce transaction data, visualizing population trends, or predicting student performance based on academic data. These projects provide a solid foundation in data manipulation, visualization, and basic predictive modeling.

Internships are essential for bridging the gap between theoretical knowledge and practical application in data analytics. Real-world projects during internships provide hands-on experience, helping individuals hone their skills and gain exposure to industry dynamics. This immersion is vital for developing problem-solving capabilities, teamwork, and a holistic understanding of analytics in a professional context.

Data analytics is essential in cybersecurity, serving as a critical element in identifying and preventing threats. Through the analysis of extensive datasets, cybersecurity professionals can proactively identify anomalies and patterns, reinforcing digital defenses against potential breaches.

Data analytics revolutionizes education by offering valuable insights into student performance, learning patterns, and predictive outcomes. Educators leverage this information to customize learning experiences, improve engagement, and optimize teaching methodologies, creating a more efficient and personalized educational environment.

Typically, the minimum qualifications for a data analyst course include a bachelor's degree in a related field such as statistics, mathematics, computer science, or business. Some courses may also consider candidates with relevant work experience or equivalent certifications.

In Kenya, Data Analysts are highly valued professionals, earning an average annual salary of KES 864,168, according to Payscale. This substantial compensation highlights the demand for skilled data analysts and the recognition of their crucial role in transforming data into valuable insights.

Internships play a crucial role in bridging the gap between theoretical knowledge and practical application in data analytics. Real-world projects provide hands-on experience, refine skills, and expose individuals to industry dynamics. This immersion is vital for developing problem-solving abilities, teamwork, and a comprehensive understanding of analytics in a professional context.

To begin learning data analytics, consider enrolling in reputable courses or pursuing degrees in the field, available on various online platforms. Engage in practical projects, participate in online communities, and stay updated on industry trends to solidify your skills. Continuous learning and hands-on experience are crucial for mastering data analytics.

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FAQ’S OF DATA ANALYST TRAINING IN KENYA

The fee for DataMites' Data Analytics Course in Kenya ranges from KES 68,128 to KES 209,491. This pricing model ensures accessibility to the comprehensive data analytics program, accommodating different budget constraints and fostering skill development for career advancement in the field.

DataMites' certified data analyst training in Kenya includes a comprehensive array of tools: Advanced Excel, MySQL, MongoDB, Git, GitHub, Atlassian BitBucket, Hadoop, Apache Pyspark, Anaconda, Google Collab, Numpy, Pandas, Tableau, Power BI. This broad toolkit ensures participants are well-versed in handling diverse data analytics tasks.

Opt for DataMites' Certified Data Analyst Course in Kenya for its flexibility, industry-relevant curriculum, expert instructors, dedicated practice lab, engaged learning community, and lifelong access. The course includes unlimited projects and placement assistance, ensuring a well-rounded learning experience for prospective data analysts.

Eligible for enrollment are beginners and intermediate learners in data analytics for the Certified Data Analyst Course by DataMites in Kenya. This career-oriented program ensures a thorough grasp of data analysis, data science foundations, statistics, visual analytics, data modeling, and predictive modeling.

The Data Analyst Course in Kenya at DataMites extends over 6 months, with 20 hours of weekly learning. With a total of 200+ learning hours, participants gain in-depth knowledge and skills in data analytics.

DataMites' Certified Data Analyst Course in Kenya stands out for its dedicated focus on advanced analytics and business insights. It offers a NO-CODE program, enabling data analytics and managers to explore advanced analytics without a programming background. The course undergoes regular updates to stay aligned with industry trends, providing a structured and efficient learning path.

Absolutely, the Certified Data Analyst Course at DataMites in Kenya includes internship opportunities. Partnering with leading Data Science companies, learners have the chance to translate their knowledge into practical solutions. Guided by DataMites experts and mentors, participants contribute to developing real-world data models that bring tangible value to businesses.

Count on DataMites for tailored assistance in Kenya, as they guide you through the intricacies of data analytics course topics, ensuring a comprehensive understanding of the subject.

Dive into the Certified Data Analyst Training in Kenya, covering foundational aspects like Data Analysis Foundation, Statistics Essentials, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, Database (SQL and MongoDB), Version Control with Git, Big Data Foundation, Python Foundation, and Certified Business Intelligence (BI) Analyst.

Simplify your payment process for the Certified Data Analytics Course at DataMites in Kenya by choosing from various options such as cash, debit cards, checks, credit cards (Visa, Mastercard, American Express), EMI, PayPal, and net banking. This ensures a seamless and adaptable payment experience.

DataMites' Certified Data Analyst Course in Kenya offers a Flexi Pass, granting participants the freedom to choose a training schedule that suits them. This adaptable option is designed to accommodate diverse timelines, enabling learners to effectively juggle professional commitments while progressing towards certification.

Successful completion of the Certified Data Analyst Course at DataMites in Kenya results in aspirants receiving IABAC Certification. This globally recognized credential validates their expertise in data analytics, significantly advancing career opportunities in the field.

In the Certified Data Analyst Course at DataMites, Kenya, a practical and hands-on learning methodology is embraced. The curriculum is enriched with real-world projects and case studies, empowering participants to gain practical experience and proficiency in data analytics techniques and tools.

DataMites' data analytics courses in Kenya offer two flexible learning options: Online Data Analytics Training in Kenya or Self-Paced Training. Participants can select the method that best accommodates their schedule and aligns with their preferred learning style.

At DataMites, if you can't attend a data analytics session in Kenya, we've got you covered with recorded sessions, facilitating convenient catch-up.

Ashok Veda, a respected Data Science coach and AI expert, leads the Certified Data Analyst Course at DataMites in Kenya, ensuring top-tier mentorship. Elite mentors and faculty members with hands-on experience from leading companies and prestigious institutes like IIMs contribute to a comprehensive and industry-relevant learning journey.

 

Tailored for personalized growth, data analytics career mentoring sessions in Kenya are one-on-one discussions with experienced mentors. Topics covered include skill development, industry trends, and strategic career planning, providing participants with valuable insights for navigating the dynamic field of data analytics in Kenya.

Yes, the Data Analyst Course offered by DataMites in Kenya emphasizes practical learning through live projects, encompassing 5+ Capstone Projects and a key Client/Live Project. This approach ensures participants acquire real-world skills and experience in the dynamic field of data analytics.

Yes, the Certified Data Analyst Course by DataMites is highly valuable in Kenya. Its non-coding emphasis makes it accessible for individuals without technical expertise, providing a unique advantage. The 3-month internship in an AI company offers practical experience, and the experience certificate, along with expert faculty guidance and prestigious IABAC Certification, solidifies its standing as a comprehensive and esteemed data analytics program.

During data analytics training sessions, participants need to furnish a valid photo ID, like a national ID card or driver's license. This is a mandatory requirement for obtaining the participation certificate and arranging any necessary certification exams.

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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