## DATA ANALYST COURSE FEE IN LUSAKA, ZAMBIA

### Live Virtual

Instructor Led Live Online

##### ZK 40,620
###### ZK 23,051

• 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

## Enquire Now

### Blended Learning

Self Learning + Live Mentoring

##### ZK 20,310
###### ZK 13,211

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

## Enquire Now

### Corporate Training

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

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## 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.

## SYLLABUS OF DATA ANALYST COURSE IN LUSAKA

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
• 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

• 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
• 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
• 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

## ABOUT DATA ANALYST TRAINING IN LUSAKA

The Data Analytics Market is on a trajectory of impressive growth, with the global market projected to reach USD 329.8 Billion by 2030. Lusaka, as the capital city of Zambia, is not immune to this transformative wave. The demand for proficient Data Analysts is on the rise in Lusaka, as businesses and industries keenly integrate data-driven strategies. To embark on a rewarding career path, consider enrolling in Data Analytics Training in Lusaka. It's the first step toward unlocking a world of opportunities in the dynamic field of data analytics.

In the heart of Lusaka, DataMites stands out as a global training institute for Data Analytics. Offering a Certified Data Analyst Course in Lusaka, this program caters to both beginners and intermediate learners, providing a solid foundation in Data Analysis, Data Science Foundation, Statistics, Visual Analytics, Data Modeling, and Predictive Modeling. The curriculum is designed with a career-oriented approach, ensuring that participants are well-equipped for the demands of the industry. Upon successful completion, receive the prestigious IABAC Certification, validating your expertise in Data Analytics.

DataMites unfolds a structured learning experience through three distinctive phases.

1. Begin your journey with Phase 1, engaging in pre-course self-study through high-quality videos designed for easy comprehension.
2. Progress to Phase 2, a rigorous 3-month duration encompassing live training, 20 hours per week, a comprehensive syllabus, hands-on projects, and mentorship by expert trainers.
3. Conclude your training with Phase 3, dedicating another 3 months to project mentoring, 5+ capstone projects, real-time internship, and earning valuable IABAC and Data Analytics Internship Certifications. Immerse yourself in a methodical learning experience with DataMites in Lusaka.

Certified Data Analyst Courses in Lusaka- Features

Expert Leadership: Ashok Veda, with over 19 years of experience, leads our Data Analytics Courses in Lusaka. As the Founder & CEO at Rubixe™, his leadership brings unparalleled expertise.

Comprehensive Curriculum: Our 6-month certified data analyst training in Lusaka features a robust curriculum with both no-code and optional Python tracks.

Learning Structure: With 20 hours of learning per week and 200+ learning hours, our program ensures a thorough understanding and mastery.

Flexible Learning: We offer a flexible learning approach, combining online data analytics courses in Lusaka with self-study, providing a personalized educational experience.

Real-World Experience: Immerse yourself in practical Data Analytics scenarios with 5+ capstone projects and a client/live project, gaining hands-on experience.

Career Support: Our commitment extends to end-to-end job support, personalized resume and data analytics interview preparation, job updates, and connections.

Exclusive Learning Community: Join our exclusive learning community, fostering collaboration and networking among like-minded individuals.

Affordable Pricing: Our data analytics course fee in Lusaka are priced affordably, ranging from ZMW 11182 to ZMW 34384, ensuring accessibility to quality Data Analytics education in Lusaka.

The Data Analytics Industry in Lusaka is experiencing notable growth, with businesses increasingly adopting data-driven approaches across diverse sectors. This trend reflects the industry's expanding influence and the growing demand for skilled Data Analysts.

Data Analysts In Lusaka,  are highly valued, as reflected in their remuneration. According to Salary Explorer, the estimated total pay for a Data Analyst is an impressive 74,200 ZMK yearly. This figure not only underscores the significance of analytical skills but also positions Data Analysts among the highly paid professionals in Zambia, highlighting the lucrative and rewarding nature of careers in Data Analytics industry.

Embark on a transformative career journey with DataMites in Lusaka. Our distinguished courses in Artificial Intelligence, Data Engineering, Python, Machine Learning, Tableau, Data Science, and more empower you with the expertise needed in today's competitive landscape. Gain a strategic advantage with our industry-aligned curriculum and practical training. DataMites is your pathway to career success in Lusaka, offering unparalleled opportunities for growth. Choose excellence, choose DataMites, and chart your course towards a prosperous future.

## ABOUT DATAMITES DATA ANALYST COURSE IN LUSAKA

Data analytics involves the process of examining, cleaning, transforming, and modeling data to extract valuable insights and inform decision-making. It encompasses various techniques and methodologies to interpret patterns, trends, and relationships within datasets, ultimately driving business strategies and outcomes.

Artificial intelligence enhances data analytics by automating tasks, discovering complex patterns, and making predictions from large datasets. AI techniques like machine learning enable predictive modeling, anomaly detection, and natural language processing, augmenting the capabilities of data analytics for deeper insights and smarter decision-making.

Data analysts are responsible for collecting, processing, and analyzing data to generate actionable insights. They clean and organize datasets, perform statistical analysis, create data visualizations, and communicate findings to stakeholders. Additionally, data analysts may be involved in developing data-driven strategies and optimizing processes to meet organizational goals.

While significant progress can be made in learning data analytics within six months, achieving proficiency depends on individual dedication, prior knowledge, and learning resources. Focused study, practical projects, and continuous learning can expedite skill development, but mastery often requires more time and real-world experience.

Essential skills for data analytics include proficiency in programming languages like Python or R, expertise in statistical analysis and data visualization tools, strong problem-solving abilities, and effective communication skills to convey insights to non-technical stakeholders.

Typically, a background in mathematics, statistics, computer science, or related fields is preferred for enrolling in a data analyst course. While some courses may have specific prerequisites, others may be accessible to individuals with a basic understanding of data analysis concepts and programming.

Key tools for learning data analytics include programming languages like Python or R, statistical packages such as Pandas, NumPy, or SciPy, database querying languages like SQL, and data visualization libraries like Matplotlib or Seaborn.

Data analytics can be challenging due to the complexity of datasets, the need for advanced analytical and programming skills, and the requirement to derive actionable insights from data. Additionally, staying updated with evolving technologies and methodologies adds to the ongoing challenge of mastering data analytics.

Glassdoor estimates the total pay for a Data Analyst in Lusaka to be an impressive ZMW 8,900 monthly, highlighting the lucrative nature of the position in the local job market.

Data Analytics Internships offer hands-on experience with real-world data sets, tools, and methodologies, providing invaluable exposure to practical applications of data analytics. Working alongside professionals allows students to apply theoretical knowledge, develop problem-solving skills, and gain insights into industry best practices, enhancing their employability and readiness for the workforce.

While coding is integral to data analytics, the extent varies. Basic proficiency in languages like Python or R is essential for data manipulation and analysis. While some roles may require extensive coding for algorithm development, others rely more on using pre-built tools and platforms.

DataMites, a prestigious institution in Lusaka, provides high-caliber data analytics courses, including Certified Data Analyst Training - No coding. Their focus on practical learning and industry applicability ensures students acquire vital skills for a thriving data analytics career.

Examples include fraud detection in financial transactions, personalized recommendation systems in e-commerce, predictive maintenance in manufacturing, healthcare analytics for disease diagnosis and treatment optimization, and social media analytics for sentiment analysis and customer engagement.

Big data analytics enables organizations to extract valuable insights from large, complex, and diverse datasets that traditional analytics tools cannot handle. It empowers data-driven decision-making, enhances operational efficiency, improves customer experiences, and drives innovation across industries by uncovering hidden patterns, trends, and correlations within vast amounts of data.

Key job roles in data analytics include data analyst, data scientist, business analyst, data engineer, and machine learning engineer. Each role involves distinct responsibilities, such as data cleaning, analysis, modeling, interpretation, and communication, contributing to organizational decision-making and strategy formulation.

Data analytics optimizes supply chain processes by improving demand forecasting accuracy, inventory management, and logistics efficiency. It enables real-time tracking of shipments, identifies potential bottlenecks, and enhances supplier performance through data-driven insights, ultimately reducing costs and improving customer satisfaction.

Innovations like artificial intelligence, machine learning, and big data processing technologies are revolutionizing data analytics. These advancements enable faster data processing, more accurate predictions, and automation of tasks, driving efficiency, scalability, and deeper insights in data analytics applications across industries.

Data analytics informs marketing strategies by analyzing customer behavior, preferences, and market trends. It facilitates targeted advertising, personalized messaging, and segmentation strategies based on demographic, psychographic, and behavioral data. By optimizing marketing efforts, data analytics enhances customer engagement, conversion rates, and overall return on investment.

Data analytics revolutionizes healthcare by improving patient outcomes, operational efficiency, and cost-effectiveness. It enables predictive analytics for disease prevention, personalized treatment plans based on patient data analysis, and population health management. Additionally, data analytics enhances healthcare delivery through optimized resource allocation, patient flow management, and quality assessment, ultimately leading to better healthcare outcomes.

Data cleaning involves identifying and handling missing values, removing duplicates, and correcting errors in the dataset. Preprocessing includes standardizing formats, scaling features, and transforming variables to ensure data quality and suitability for analysis. Techniques like outlier detection, imputation, and normalization are applied to prepare the data for analysis, ensuring accurate and reliable insights extraction.

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## FAQ’S OF DATA ANALYST TRAINING IN LUSAKA

DataMites stands out for its Certified Data Analyst Course in Lusaka, offering flexible learning, a curriculum tailored for industry relevance, expert instructors, dedicated practice lab access, vibrant learning community engagement, and lifelong learning resources. With unlimited project opportunities and placement assistance, DataMites paves the path for a successful career in data analytics.

Participants in the Certified Data Analyst Training in Lusaka will delve into essential topics such as Data Analysis Foundation, Statistics Essentials, Data Analysis Associate, Advanced Data Analytics, Predictive Analytics with Machine Learning, Database Management using SQL and MongoDB, Version Control with Git, Big Data Foundation, Python Foundation, and Certified Business Intelligence (BI) Analyst.

Beginners and intermediate learners interested in data analytics are eligible for the Certified Data Analyst Training in Lusaka by DataMites. The program emphasizes career development, covering essential topics like data analysis, statistics, visual analytics, and predictive modeling.

DataMites' Data Analyst Course in Lusaka spans 6 months, involving 20 hours of weekly learning. Participants can expect to complete over 200 learning hours, equipping them with essential skills for the data analytics industry.

Participants in DataMites' certified data analyst training in Lusaka will learn to utilize tools like GitHub, Atlassian BitBucket, and Hadoop for effective data management.

DataMites' Certified Data Analyst Course in Lusaka is tailored for advanced analytics and business insights, featuring a NO-CODE approach suitable for learners without a programming background.

The fee for DataMites' Data Analytics Course in Lusaka ranges from ZMW 11,182 to ZMW 34,384. The variation in fees may be due to factors such as course duration, curriculum complexity, and additional resources included in the program.

Absolutely, DataMites provides extensive support for participants to grasp data analytics course topics in Lusaka. Backed by seasoned instructors, interactive learning resources, tailored mentoring sessions, and a collaborative learning environment, participants receive continuous guidance to ensure their understanding and success in the program.

Payment for the Certified Data Analytics Course at DataMites in Lusaka can be made via cash, debit card, check, credit card, EMI, PayPal, Visa, Mastercard, American Express, or net banking.

DataMites provides diverse learning methods for its data analytics courses in Lusaka, such as online data analytics training in Lusaka and self-paced learning. Participants have the flexibility to engage in interactive online sessions or proceed through course materials independently. These adaptable learning options accommodate a wide range of preferences and schedules.

At DataMites in Lusaka, the Certified Data Analyst Course is conducted by Ashok Veda and a team of elite mentors, ensuring participants receive top-notch instruction in Data Science and AI. With real-world experience from leading companies and esteemed institutes like IIMs, trainers provide invaluable insights and guidance throughout the program.

DataMites' Flexi Pass for the Certified Data Analyst Course in Lusaka provides participants with the flexibility to tailor their learning experience according to their schedule. This option allows learners to access course content and attend sessions at their preferred times, accommodating personal or professional commitments effectively.

Yes, participants who complete the Certified Data Analyst Course in Lusaka at DataMites will be awarded the prestigious IABAC Certification. This renowned certification demonstrates their competence in data analytics, bolstering their professional reputation and opening doors to diverse career opportunities in the field.

DataMites adopts a case study-oriented approach for its Certified Data Analyst Course in Lusaka. Through practical application, participants engage in analyzing real-world scenarios, facilitating hands-on experience in data analysis methods. This methodology strengthens understanding and equips learners with the skills to address industry challenges confidently.

In the event of missing a data analytics session in Lusaka, DataMites provides session recordings for convenient review. Furthermore, supplementary study materials and resources are available to cover any missed topics. This ensures you remain aligned with the curriculum, enabling you to stay on track with your learning journey despite missed sessions.

Participants need to present valid photo identification, such as a national ID card or driver's license, during training sessions. This is mandatory for obtaining the participation certificate and scheduling certification exams. It ensures accurate documentation and accountability throughout the training process.

Absolutely, DataMites provides live projects as part of the data analyst course in Lusaka. Participants undertake 5+ capstone projects and collaborate on 1 client/live project. These practical endeavors offer valuable hands-on experience, enabling learners to apply their knowledge in real-world contexts, bolstering their expertise and industry readiness.

DataMites structures its data analytics career mentoring sessions in Lusaka to offer personalized guidance and support to participants. Through one-on-one meetings with experienced mentors, individuals receive tailored career advice, insights, and strategies to advance their careers in the data analytics industry effectively.

DataMites' Certified Data Analyst Course offers immense value in Lusaka. It's the most comprehensive non-coding course, making data analytics accessible to non-technical backgrounds. Participants gain three months of internship experience at an AI company, along with an experience certificate and prestigious IABAC Certification, ensuring industry recognition and career prospects.

Absolutely, DataMites provides internships alongside the Certified Data Analyst Course in Lusaka. Learners benefit from exclusive partnerships with leading Data Science companies, gaining practical experience. This hands-on opportunity enables them to implement their knowledge in real-world projects, guided by DataMites experts and mentors, enriching their skill set and industry relevance.

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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