DATA SCIENCE CERTIFICATION AUTHORITIES

Data Science Course Features

DATA SCIENCE LEAD MENTORS

DATA SCIENCE COURSE FEE IN LUSAKA, ZAMBIA

Live Virtual

Instructor Led Live Online

ZK 40,620
ZK 26,075

  • IABAC® & NASSCOM® Certification
  • 8-Month | 700 Learning Hours
  • 120-Hour Live Online Training
  • 25 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

ZK 24,370
ZK 15,856

  • Self Learning + Live Mentoring
  • IABAC® & NASSCOM® Certification
  • 1 Year Access To Elearning
  • 25 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Leaner 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

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

Enquire Now

UPCOMING DATA SCIENCE ONLINE CLASSES IN LUSAKA

BEST DATA SCIENCE 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 SCIENCE COURSE

Why DataMites Infographic

SYLLABUS OF DATA SCIENCE COURSE IN LUSAKA

MODULE 1: DATA SCIENCE ESSENTIALS 

 • Introduction to Data Science
 • Evolution of Data Science
 • Big Data Vs Data Science
 • Data Science Terminologies
 • Data Science vs AI/Machine Learning
 • Data Science vs Analytics

MODULE 2: DATA SCIENCE DEMO

 • Business Requirement: Use Case
 • Data Preparation
 • Machine learning Model building
 • Prediction with ML model
 • Delivering Business Value.

MODULE 3: ANALYTICS CLASSIFICATION 

 • Types of Analytics
 • Descriptive Analytics
 • Diagnostic Analytics
 • Predictive Analytics
 • Prescriptive Analytics
 • EDA and insight gathering demo in Tableau

MODULE 4: DATA SCIENCE AND RELATED FIELDS

 • Introduction to AI
 • Introduction to Computer Vision
 • Introduction to Natural Language Processing
 • Introduction to Reinforcement Learning
 • Introduction to GAN
 • Introduction to Generative Passive Models

MODULE 5: DATA SCIENCE ROLES & WORKFLOW

 • Data Science Project workflow
 • Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
 • Data Science Project stages.

MODULE 6: MACHINE LEARNING INTRODUCTION

 • What Is ML? ML Vs AI
 • ML Workflow, Popular ML Algorithms
 • Clustering, Classification And Regression
 • Supervised Vs Unsupervised

MODULE 7: DATA SCIENCE INDUSTRY APPLICATIONS

 • Data Science in Finance and Banking
 • Data Science in Retail
 • Data Science in Health Care
 • Data Science in Logistics and Supply Chain
 • Data Science in Technology Industry
 • Data Science in Manufacturing
 • Data Science in Agriculture

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
 • Empirical Rule and Outliers
 • Central Limit Theorem
 • Normality Testing
 • Skewness & Kurtosis
 • Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
 • 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: MACHINE LEARNING INTRODUCTION 

 • What Is ML? ML Vs AI
 • Clustering, Classification And Regression
 • Supervised Vs Unsupervised

MODULE 2:  PYTHON NUMPY  PACKAGE 

 • Introduction to Numpy Package
 • Array as Data Structure
 • Core Numpy functions
 • Matrix Operations, Broadcasting in Arrays

MODULE 3:  PYTHON PANDAS PACKAGE 

 • Introduction to Pandas package
 • Series in Pandas
 • Data Frame in Pandas
 • File Reading in Pandas
 • Data munging with Pandas

MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib

 • Visualization Packages (Matplotlib)
 • Components Of A Plot, Sub-Plots
 • Basic Plots: Line, Bar, Pie, Scatter

MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN

 • Seaborn: Basic Plot
 • Advanced Python Data Visualizations

MODULE 6: ML ALGO: LINEAR REGRESSSION

 • Introduction to Linear Regression
 • How it works: Regression and Best Fit Line
 • Modeling and Evaluation in Python

MODULE 7: ML ALGO: LOGISTIC REGRESSION

 • Introduction to Logistic Regression
 • How it works: Classification & Sigmoid Curve
 • Modeling and Evaluation in Python

MODULE 8: ML ALGO: K MEANS CLUSTERING

 • Understanding Clustering (Unsupervised)
 • K Means Algorithm
 • How it works : K Means theory
 • Modeling in Python

MODULE 9: ML ALGO: KNN

 • Introduction to KNN
 • How It Works: Nearest Neighbor Concept
 • Modeling and Evaluation in Python

MODULE 1: FEATURE ENGINEERING 

 • Introduction to Feature Engineering
 • Feature Engineering Techniques: Encoding, Scaling, Data Transformation
 • Handling Missing values, handling outliers
 • Creation of Pipeline
 • Use case for feature engineering

MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

 • Introduction to SVM
 • How It Works: SVM Concept, Kernel Trick
 • Modeling and Evaluation of SVM in Python

MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)

 • Building Blocks Of PCA
 • How it works: Finding Principal Components
 • Modeling PCA in Python

MODULE 4:  ML ALGO: DECISION TREE 

 • Introduction to Decision Tree & Random Forest
 • How it works
 • Modeling and Evaluation in Python

MODULE 5: ENSEMBLE TECHNIQUES - BAGGING 

 • Introduction to Ensemble technique 
 • Bagging and How it works
 • Modeling and Evaluation in Python

MODULE 6: ML ALGO: NAÏVE BAYES

 • Introduction to Naive Bayes
 • How it works: Bayes' Theorem
 • Naive Bayes For Text Classification
 • Modeling and Evaluation in Python

MODULE 7: GRADIENT BOOSTING, XGBOOST

 • Introduction to Boosting and XGBoost
 • How it works?
 • Modeling and Evaluation of in Python

MODULE 1: TIME SERIES FORECASTING - ARIMA 

 • What is Time Series?
 • Trend, Seasonality, cyclical and random
 • Stationarity of Time Series
 • Autoregressive Model (AR)
 • Moving Average Model (MA)
 • ARIMA Model
 • Autocorrelation and AIC
 • Time Series Analysis in Python 

MODULE 2: SENTIMENT ANALYSIS 

 • Introduction to Sentiment Analysis
 • NLTK Package
 • Case study: Sentiment Analysis on Movie Reviews

MODULE 3: REGULAR EXPRESSIONS WITH PYTHON 

 • Regex Introduction
 • Regex codes
 • Text extraction with Python Regex

MODULE 4:  ML MODEL DEPLOYMENT WITH FLASK 

 • Introduction to Flask
 • URL and App routing
 • Flask application – ML Model deployment

MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL

 • MS Excel core Functions
 • Advanced Functions (VLOOKUP, INDIRECT..)
 • Linear Regression with EXCEL
 • Data Table
 • Goal Seek Analysis
 • Pivot Table
 • Solving Data Equation with EXCEL

MODULE 6:  AWS CLOUD FOR DATA SCIENCE

 • Introduction of cloud
 • Difference between GCC, Azure, AWS
 • AWS Service ( EC2 instance)

MODULE 7: AZURE FOR DATA SCIENCE

 • Introduction to AZURE ML studio
 • Data Pipeline
 • ML modeling with Azure

MODULE 8:  INTRODUCTION TO DEEP LEARNING

 • Introduction to Artificial Neural Network, Architecture
 • Artificial Neural Network in Python
 • Introduction to Convolutional Neural Network, Architecture
 • Convolutional Neural Network in Python

MODULE 1: DATABASE INTRODUCTION 

 • DATABASE Overview
 • Key concepts of database management
 • Relational Database Management System
 • CRUD operations

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 function: 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

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
 • Git Essentials: Copy & User Setup
 • Mastering Git and GitHub

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
 • Editing Commits
 • Commit command Amend flag
 • Git reset and revert

MODULE 5: GIT WITH GITHUB AND BITBUCKET

 • Creating GitHub Account
 • Local and Remote Repo
 • Collaborating with other developers

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 SCIENCE COURSES IN LUSAKA

DATA SCIENCE COURSE REVIEWS

ABOUT DATA SCIENTIST TRAINING IN LUSAKA

In the expansive field of data science, Forbes highlights that 5% of positions in data analytics and science are concentrated in sectors like IT, finance, insurance, and related professional services. This statistic underscores the increasing significance of data-driven insights across various industries.

Shifting our focus to Lusaka, the data science industry in Lusaka is witnessing notable growth. Lusaka stands as a pivotal center for data science innovation, with businesses recognizing the transformative potential of leveraging data for informed decision-making. The city is emerging as a hub for professionals seeking rewarding careers in the evolving landscape of data science.

Navigating the landscape of data science, DataMites emerges as a leading institute, providing global training in the Certified Data Scientist Course in Lusaka designed for both beginners and intermediate learners. Renowned as the world's most popular, comprehensive, and job-oriented data science program, our courses are curated to ensure participants are prepared for success. Moreover, our IABAC-endorsed certification solidifies the acquired proficiency, establishing a credible recognition in the field of data science.

Embarking on a learning journey at DataMites entails a structured three-phase training approach, ensuring a thorough understanding of data science. 

  1. The initial phase involves pre-course self-study, facilitated by high-quality videos employing an easy learning approach. 
  2. Transitioning to the second phase, live training unfolds, featuring a comprehensive syllabus, hands-on projects, and guidance from expert trainers and mentors. 
  3. The final phase encompasses a 4-month project, including mentoring, an internship, and active engagement in 20 capstone projects, culminating in a client/live project. Completion of this holistic program earns participants a valuable experience certificate, validating their acquired expertise.

Choosing Data Science Training in Lusaka

Ashok Veda and Faculty:

At DataMites, Ashok Veda, with over 19 years in data science and analytics, leads our top-tier education. As the Founder & CEO at Rubixe™, his expertise in data science and AI ensures an unparalleled learning experience.

Course Curriculum:

Immerse yourself in an extensive 8-month program, spanning 700+ learning hours, providing a comprehensive understanding of data science.

Global Certification - IABAC® Certification:

Upon completion, receive the prestigious IABAC® Certification, globally recognized for validating proficiency in data science.

Flexible Learning Options:

Tailor your learning with online data science courses and self-study modules, offering flexibility to navigate the course at your own pace.

Projects and Internship Opportunities:

Engage in hands-on learning with 20 capstone projects and 1 client project, using real-world data, fostering active interaction and practical understanding.

Career Guidance and Job References:

Benefit from end-to-end job support, personalized resume crafting, interview preparation, and continuous updates on job opportunities and industry connections.

DataMites Exclusive Learning Community:

Join our exclusive learning community, fostering collaboration, networking, and knowledge-sharing among fellow data science enthusiasts.

Affordable Pricing and Scholarships:

DataMites offers affordable pricing, with data science course fees in Lusaka ranging from ZMW 13,653 to ZMW 34,137. Explore scholarship opportunities to make quality education accessible.

The data science sector in Lusaka is witnessing substantial growth, as businesses in various industries acknowledge the transformative potential of insights derived from data. With organizations increasingly adopting data-centric approaches, Lusaka emerges as a thriving center for innovation and practical applications of data science, offering enticing career prospects.

In Lusaka, Data Scientists receive competitive remuneration, underscoring their pivotal role in extracting actionable insights. According to Salary Explorer, data science professionals in this role typically earn around 9,960 ZMW annually. This attractive compensation not only draws in top talent but also reinforces Lusaka's reputation as an attractive destination for individuals seeking fulfilling careers in the dynamic and rapidly evolving field of data science.

DataMites stands as the epitome of excellence in data scientist training course, offering a diverse range of courses. Explore the realms of artificial intelligence, data engineering, data analytics, machine learning, Python, tableau, and more with our comprehensive programs. At DataMites, we pave the way for your career success, providing unparalleled education and expertise. Choose DataMites as your trusted partner for an enriching learning experience that opens doors to rewarding opportunities in Lusaka and beyond.

ABOUT DATAMITES DATA SCIENCE COURSE IN LUSAKA

Statistics plays a crucial role in data science, aiding analysts in drawing meaningful conclusions from data. This encompasses descriptive statistics for data summarization and inferential statistics for making predictions and decisions based on sampled data.

Data Science entails extracting insights and knowledge from data using techniques such as statistics, machine learning, and data analysis, covering the entire data lifecycle from collection to visualization.

While a bachelor's degree in a related field is common, many Data Scientists hold advanced degrees such as a master's or Ph.D. Strong foundational skills in mathematics, programming, and relevant experience are equally crucial.

Data Science operates by collecting and analyzing extensive datasets to reveal patterns, trends, and insights. It employs statistical methods, machine learning algorithms, and programming languages like Python or R to extract valuable information.

The Certified Data Scientist Course stands out as a premier option in Lusaka. This comprehensive program covers essential data science skills, including programming, statistics, and machine learning, providing hands-on experience for successful careers in the dynamic field.

Data Science in finance is employed for risk management, fraud detection, customer segmentation, and algorithmic trading. It utilizes predictive modeling and analytics to optimize decision-making processes, enhance customer experiences, and identify anomalies in financial transactions.

Individuals with backgrounds in mathematics, statistics, computer science, or related fields can enroll in Data Science Certification Courses. These courses are also beneficial for professionals aiming to enhance analytical skills or transition into the field.

Vital skills for aspiring Data Scientists include proficiency in programming languages, data manipulation, statistical analysis, machine learning, and effective communication to convey findings convincingly.

Start by building a strong foundation in mathematics and programming. Gain practical experience with real-world datasets, explore online courses, engage in projects, and create a portfolio showcasing your skills. Networking with professionals in the field also provides valuable insights.

Common challenges include data quality issues, model interpretability, and scalability. Addressing these challenges involves meticulous data preprocessing, the utilization of explainable AI techniques, and optimizing algorithms for efficient processing.

Data Science is pivotal in e-commerce, analyzing customer behavior, preferences, and transaction data. Through recommendation systems driven by machine learning algorithms, it personalizes user experiences, suggests products, and elevates customer engagement, ultimately boosting sales and satisfaction.

In Lusaka, Data Scientists typically begin as analysts, advancing to senior roles or specializing in positions like machine learning engineers or data architects. Career growth is often achieved through continuous learning, networking, and hands-on experience.

Internships offer practical exposure to real-world projects, fostering hands-on skill development and industry insight. They not only enhance resumes but also facilitate networking, often leading to full-time job opportunities.

Enrolling in Data Science Bootcamps can be beneficial for rapidly acquiring skills. These programs provide practical experience, mentorship, and networking, expediting entry into the field. Success, however, depends on individual dedication and the quality of the chosen bootcamp.

Data Scientists are responsible for collecting, processing, and analyzing extensive datasets to derive actionable insights. They create predictive models, design experiments, and communicate findings to inform strategic decision-making. Collaborating with cross-functional teams, they contribute to problem-solving and drive innovation within the organization.

Data Science empowers retailers to analyze customer behavior, preferences, and purchase history, facilitating effective segmentation. Through the use of machine learning algorithms, businesses can customize shopping experiences, suggest products, and optimize marketing strategies, ultimately enhancing customer satisfaction and loyalty.

The Data Science project lifecycle involves defining objectives, data collection, preprocessing, exploratory data analysis, model development, validation, deployment, and continuous monitoring. Each stage is pivotal to ensure alignment with business objectives and deliver valuable insights.

In manufacturing and supply chain management, Data Science optimizes processes by predicting equipment failures, improving demand forecasting, and refining inventory management. This enhances operational efficiency, cuts costs, and streamlines overall supply chain operations.

As per Salary Explorer, Data Scientists in Lusaka, can anticipate an annual salary of approximately 9,960 ZMK. This reflects the recognition of their specialized skills in data analysis, contributing to informed decision-making. Their role is crucial in fostering innovation and efficiency across various industries in the vibrant city of Lusaka.

Data Science is extensively utilized in sectors like finance, healthcare, e-commerce, manufacturing, and telecommunications. Its adaptable tools and methodologies contribute to enhanced decision-making, operational efficiency, and innovation across a variety of industries.

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FAQ’S OF DATA SCIENCE TRAINING IN LUSAKA

Trainers at DataMites are chosen based on their elite status, with faculty members possessing real-time experience from top companies and esteemed institutes like IIMs conducting the data science training sessions.

DataMites provides a diverse range of data science certifications in Lusaka, including the well-known Certified Data Scientist course and specialized programs such as Data Science for Managers and Data Science Associate. These cater to various skill levels and professional domains, encompassing Marketing, Operations, Finance, HR, and more.

For newcomers in Lusaka, DataMites offers foundational data science training through courses like Certified Data Scientist, providing comprehensive skills. The Data Science in Foundation track and the Diploma in Data Science ensure a well-rounded learning experience, serving as ideal starting points for those entering the data science field.

The duration of DataMites' data scientist courses in Lusaka ranges from 1 to 8 months, varying based on the course level and specific program.

No prerequisites are required for enrolling in the Certified Data Scientist Training in Lusaka. Tailored for beginners and intermediate learners in data science, the course ensures accessibility for individuals looking to enter the field.

DataMites' online data science training in Lusaka provides the flexibility to learn from any location, overcoming geographical constraints. The interactive online platform fosters engagement through discussions, forums, and collaborative activities, enriching the overall data science training experience.

The cost structure for DataMites' data science training in Lusaka is designed to be flexible, with fees ranging from ZMW 13,653 to ZMW 34,137. This adaptability ensures accessibility for a wide range of participants, allowing them to choose a plan that aligns with their budget constraints. The training curriculum encompasses a holistic approach, incorporating practical skills and knowledge applicable to various data science roles in Lusaka.

Certainly, DataMites provides specialized data science courses for Lusakaian professionals, including Statistics, Python, and Certified Data Scientist Operations. Tailored options like Data Science with R Programming and Certified Data Scientist Courses in Marketing, HR, and Finance specifically target working professionals, ensuring focused skill enhancement.

The DataMites Certified Data Scientist Course in Lusaka is globally recognized in Data Science and Machine Learning. Updated to align with industry needs, it adopts a job-oriented approach, equipping participants with crucial skills and knowledge for success in the dynamic field of data science.

Yes, participants need to provide a valid photo identification proof, such as a national ID card or driver's license, to receive their participation certificate and, if necessary, to schedule the certification exam during the data science training sessions.

Certainly, participants who successfully complete the data science course in Lusaka with DataMites receive a prestigious certification, validating their proficiency in the field.

Yes, DataMites offers a trial class option in Lusaka, allowing participants to preview the training content and experience the learning environment before committing to the fee.

Absolutely, DataMites offers Data Science Courses with internship opportunities in Lusaka, providing valuable hands-on experience with AI companies.

The Flexi-Pass at DataMites in Lusaka offers participants flexible learning options, enabling them to customize their training schedule based on personal preferences. This accommodates busy schedules, ensuring individuals can pursue data science training at their convenience.

The recommended option for managers or leaders seeking to incorporate data science into decision-making processes is the "Data Science for Managers" course at DataMites.

Certainly, DataMites ensures the inclusion of live projects in their Data Scientist Course in Lusaka, featuring over 10 capstone projects and hands-on client/live project experiences.

Certainly, DataMites in Lusaka offers help sessions for participants, providing additional support and clarification on specific data science topics to ensure a thorough understanding.

Upon completion of Data Science Training in Lusaka, DataMites awards participants with IABAC Certification, acknowledging their expertise in data science.

DataMites' career mentoring sessions in Lusaka follow an interactive format, guiding participants on industry trends, resume building, and interview preparation to enhance their employability in the data science field.

DataMites provides data science course training in Lusaka through online data science course training in Lusaka and self-paced methods, offering flexibility and personalized learning opportunities.

DataMites understands that participants might miss a training session in Lusaka due to unforeseen circumstances. In such cases, recorded sessions are accessible for review, enabling participants to catch up on missed content. Additionally, one-on-one sessions with trainers are available to address queries and clarify concepts covered during the missed session, ensuring a comprehensive learning experience.

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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