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MODULE 1 : ARTIFICIAL INTELLIGENCE OVERVIEW
• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence
• Why Artificial Intelligence Now?
• Areas Of Artificial Intelligence
• AI Vs Data Science Vs Machine Learning
MODULE 2 : DEEP LEARNING INTRODUCTION
• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks
MODULE3 : TENSORFLOW FOUNDATION
• TensorFlow Structure and Modules
• Hands-On:ML modeling with TensorFlow
MODULE 4 : COMPUTER VISION INTRODUCTION
• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network
MODULE 5 : NATURAL LANGUAGE PROCESSING (NLP)
• NLP Introduction
• Bag of Words Models
• Word Embedding
• Hands-On:BERT Algorithm
MODULE 6 : AI ETHICAL ISSUES AND CONCERNS
• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai:Ethics, Bias, And Trust
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 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 REGRESSION
• 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
• Cross join
• Self 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
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
MODULE 2: HDFS AND MAP REDUCE
MODULE 3: PYSPARK FOUNDATION
MODULE 4: SPARK SQL and 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
MODULE 1: NEURAL NETWORKS
• Structure of neural networks
• Neural network - core concepts(Weight initialization)
• Neural network - core concepts(Optimizer)
• Neural network - core concepts(Need of activation)
• Neural network - core concepts(MSE & RMSE)
• Feed forward algorithm
• Backpropagation
MODULE 2: IMPLEMENTING DEEP NEURAL NETWORKS
• Introduction to neural networks with tf2.X
• Simple deep learning model in Keras (tf2.X)
• Building neural network model in TF2.0 for MNIST dataset
MODULE 3: DEEP COMPUTER VISION - IMAGE RECOGNITION
• Convolutional neural networks (CNNs)
• CNNs with Keras-part1
• CNNs with Keras-part2
• Transfer learning in CNN
• Flowers dataset with tf2.X(part-1)
• Flowers dataset with tf2.X(part-2)
• Examining x-ray with CNN model
MODULE 4 : DEEP COMPUTER VISION - OBJECT DETECTION
• What is Object detection
• Methods of Object Detections
• Metrics of Object detection
• Bounding Box regression
• labelimg
• RCNN
• Fast RCNN
• Faster RCNN
• SSD
• YOLO Implementation
• Object detection using cv2
MODULE 5: RECURRENT NEURAL NETWORK
• RNN introduction
• Sequences with RNNs
• Long short-term memory networks(part 1)
• Long short-term memory networks(part 2)
• Bi-directional RNN and LSTM
• Examples of RNN applications
MODULE 6: NATURAL LANGUAGE PROCESSING (NLP)
• Introduction to Natural language processing
• Working with Text file
• Working with pdf file
• Introduction to regex
• Regex part 1
• Regex part 2
• Word Embedding
• RNN model creation
• Transformers and BERT
• Introduction to GPT (Generative Pre-trained Transformer)
• State of art NLP and projects
MODULE 7: PROMPT ENGINEERING
• Introduction to Prompt Engineering
• Understanding the Role of Prompts in AI Systems
• Design Principles for Effective Prompts
• Techniques for Generating and Optimizing Prompts
• Applications of Prompt Engineering in Natural Language Processing
MODULE 8: REINFORCEMENT LEARNING
• Markov decision process
• Fundamental equations in RL
• Model-based method
• Dynamic programming model free methods
MODULE 9: DEEP REINFORCEMENT LEARNING
• Architectures of deep Q learning
• Deep Q learning
• Reinforcement Learning Projects with OpenAI Gym
MODULE 10: Gen AI
• Gan introduction, Core Concepts, and Applications
• Core concepts of GAN
• GAN applications
• Building GAN model with TensorFlow 2.X
• Introduction to GPT (Generative Pre-trained Transformer)
• Building a Question answer bot with the models on Hugging Face
MODULE 11: Gen AI
• Introduction to Autoencoder
• Basic Structure and Components of Autoencoders
• Types of Autoencoders: Vanilla, Denoising, Variational, Sparse, and Convolutional Autoencoders
• Training Autoencoders: Loss Functions, Optimization Techniques
• Applications of Autoencoders: Dimensionality Reduction, Anomaly Detection, Image
AI is the future, and DataMites helped me step into it! The online Data Science course was easy to follow, and the tutors explained everything clearly. Even after completing my course, the team supported me with placements and I successfully cracked a job! Grateful to DataMites for shaping my career.
I joined DataMites to learn Data Science and gained strong skills in ML, DL, and Statistics. Through my internship at Rubixe AI Solutions, I worked on real-world projects, which helped me land job interviews and an offer. The trainers were very supportive and explained every concept clearly. Thanks to DataMites, my data science journey was smooth and successful.
My mentor, a DataMites alumnus, suggested I join the Data Science course, and it was one of my best decisions! The offline classes were interactive and full of guidance, and I got the chance to intern at Rubixe AI Solutions. The placement team was very supportive and helped me perfect my resume. Truly thankful to DataMites for this amazing learning journey.
I joined DataMites after completing my engineering in Computer Science and became a Certified Data Scientist. With their support, I completed an internship at Rubix AI Solutions and now work as an AI intern at Inventor Hub. DataMites made learning statistics and data science easy with clear guidance and great materials. I’m truly thankful for their support.
I joined DataMites for the Certified Data Scientist online course, and it was a great experience! The real-time projects helped me apply my learning practically, and the placement support added real value to my career restart after 8 years. DataMites is the perfect platform to learn, grow, and begin your data science journey.
I joined DataMites for the Data Science course and learned Python, SQL, Machine Learning, and Deep Learning through hands-on projects. The trainers are excellent at explaining concepts clearly. The placement team is very supportive and provides great opportunities thanks to DataMites for helping me start my data science journey.
I joined DataMites after my B.Tech when I developed an interest in Data Science and Machine Learning. The training was well-structured, and I even got an internship opportunity that helped me gain real experience. The placement team was super supportive and guided me through every step. Thanks to DataMites, I’m now placed at Horizon Broadband Pvt. Ltd.
I joined DataMites after completing my BCA, and it was the best decision for my data science career! I learned Python, SQL, Machine Learning, Deep Learning, and NLP, and worked on several capstone and client projects that gave me real-world experience. The trainers and team were always friendly and supportive, patiently clearing all my doubts. Thanks to DataMites, I’m confident and job-ready in data science
I started my data science journey with DataMites, and it’s been an amazing experience! The Certified Data Scientist Program helped me learn everything from scratch through hands-on projects and an internship. The placement team was incredibly supportive, always ready to clarify my doubts. Thanks to their guidance, I successfully got placed. Highly recommend DataMites to anyone starting their data science career
Enrolling at DataMites was one of the best decisions I made for my career. Coming from a freelance writing background with no prior technical experience, I was initially unsure about stepping into the field of data analytics. But DataMites made the transition smooth and achievable. I’d highly recommend them to anyone looking for a guided, industry-ready data analytics course. A true DataMites success story!
Most introductory artificial intelligence courses in Nairobi are open to beginners with basic computer skills and an interest in technology. Advanced programs may require a background in mathematics, statistics, computer science, or programming, particularly Python.
An online AI course in Nairobi can suit school leavers who meet the entry requirements, university students, graduates, working professionals, entrepreneurs, and people changing careers. Beginners can start with AI fundamentals, while learners with technical experience can choose specialized training in machine learning, deep learning, or generative AI.
The duration of an online artificial intelligence course in Nairobi depends on its depth, learning format, and practical requirements.
Introductory courses: A few days to 8 weeks.
Intermediate courses: Approximately 2 to 6 months.
Professional certification programs: Around 6 to 12 months.
Part-time learners may need additional time to complete assignments and projects.
As a general budgeting estimate, short online AI courses may cost approximately KES 1,000 to 15,000, while comprehensive professional programs may range from KES 30,000 to 150,000 or more. These are indicative estimates, not verified market averages or official fees; the actual price depends on course duration, curriculum, practical projects, instructor support, and certification.
Completing an AI course can help learners prepare for roles across technology, finance, healthcare, telecommunications, agriculture, and other sectors, depending on their qualifications and practical experience. Potential career paths include:
AI or machine learning engineer
Data scientist or data analyst
Natural language processing (NLP) specialist
Computer vision engineer or AI software developer
According to MyJobMag Kenya, a junior AI solutions engineer position in Nairobi advertised a salary of KES 50,000 to 100,000 per month. Actual earnings depend on experience, technical skills, employer, and job responsibilities.
Yes, working professionals can study through flexible online artificial intelligence courses in Nairobi, including evening classes, weekend sessions, and self-paced learning where available. Applying AI techniques to familiar workplace tasks can help them practice skills while balancing their existing responsibilities.
No, prior programming experience is not essential for beginner-level AI courses, which often introduce basic concepts before coding. However, learning Python, mathematics, statistics, and data handling is important for more technical work in machine learning and AI development.
An online artificial intelligence course in Nairobi may cover technical foundations and practical problem-solving skills, depending on the curriculum.
Python programming and data preparation
Machine learning algorithms and model evaluation
Deep learning and neural networks
Generative AI tools, prompt design, and responsible AI use
Practical projects can help learners understand how these methods apply to real business problems.
AI-related roles in Nairobi include machine learning engineer, AI engineer, data scientist, data analyst, AI software developer, and NLP specialist. Available positions differ in their programming requirements, mathematical depth, and business focus, so the right role depends on the learner's skills and experience.
Yes, people from business, finance, marketing, operations, and other non-technical fields can begin with AI fundamentals and gradually develop their technical knowledge. Roles involving AI product coordination, business analysis, workflow automation, and AI-assisted content work may offer relevant starting points, although technical positions generally require more specialized training.
Python is widely used because it supports machine learning, data analysis, deep learning, and generative AI development through libraries such as PyTorch and TensorFlow. Other useful languages include R for statistical analysis, SQL for working with databases, and JavaScript for integrating AI features into web applications.
Artificial intelligence can be a worthwhile career path for people who enjoy technology, analytical thinking, and solving practical problems. Kenya's National AI Strategy identifies AI infrastructure, data and AI governance, and research, innovation, and commercialization as strategic priorities, although career outcomes depend on skills, experience, and employer demand.
Kenya is building its AI ecosystem through national policy, digital infrastructure initiatives, research, and skills development, creating opportunities for organizations to explore AI applications. The Ministry of Information, Communications and the Digital Economy discussed these priorities in its work on Kenya's AI policy development; however, this does not establish a specific growth rate for AI job vacancies in Nairobi.
Artificial intelligence (AI) is the broad field of developing computer systems that perform tasks associated with human intelligence, such as reasoning, language understanding, and decision-making. Machine learning is a branch of AI in which systems learn patterns from data, while deep learning is a type of machine learning that uses layered neural networks to handle complex tasks such as image recognition and speech processing.
Generative AI tools can help professionals draft documents, summarize information, support customer service, generate software code, and analyze content more efficiently. Businesses in Nairobi can explore these applications in finance, technology, education, and other sectors, but outputs should be checked for accuracy, privacy risks, bias, and compliance with relevant data protection requirements.
Skills in Python, machine learning, data engineering, cloud-based AI, generative AI, and model evaluation are useful areas for continued learning. Responsible AI practices, data privacy, cybersecurity, critical thinking, and communication are also important as organizations seek to develop and use AI systems safely and effectively.
Nairobi's AI outlook will be influenced by Kenya's investment in digital infrastructure, workforce development, research, and responsible technology governance. The government's Kenya AI Strategy outlines national priorities for AI development, including applications in agriculture, healthcare, finance, and education, but the pace of adoption and the creation of new jobs will depend on implementation, investment, and business needs.
Learners interested in the DataMites AI Engineer course in Nairobi can contact our team to confirm the recommended educational background and any prerequisites. A basic understanding of programming, mathematics, or statistics can be helpful when learning artificial intelligence concepts.
The DataMites Artificial Intelligence course in Nairobi runs for 9 months with 780+ learning hours. This duration allows learners to study AI concepts and build their understanding through structured learning activities.
To enroll, visit the DataMites website, navigate to the Artificial Intelligence course page, and review the program details. You can contact the DataMites team to confirm the current batch schedule, available learning options, and registration process before completing your enrollment.
Learners in Nairobi can inquire about online learning options for DataMites AI training. Contact our team to confirm the availability of live online classes, upcoming batches, and the schedule that best suits your learning needs.
The stated fee range for the DataMites Artificial Intelligence course in Nairobi is KES 131,430 to KES 220,000. The applicable fee may depend on the selected course option, so contact the DataMites team to confirm the current fee and payment details before registering.
After completing the relevant program requirements, learners can earn an IABAC certification. NASSCOM FutureSkills Certification is also available for eligible NRI learners; contact the DataMites team to confirm the applicable certification requirements.
The DataMites Artificial Intelligence course may interest students, graduates, working professionals, and individuals looking to develop their AI knowledge or explore a new technical field. If you are unsure whether your background is suitable, our team can help clarify the recommended preparation before you enroll.
DataMites offers course options that learners can inquire about based on their preferred learning format and schedule. Contact our team to confirm the currently available online learning modes, batch timings, and whether a flexible option suits your needs.
The availability of an internship and specific real-time projects should be confirmed with the DataMites team before enrollment. Ask for the current curriculum details to understand which practical activities, assignments, and project components are included in your selected AI course option.
You can contact the DataMites team to ask whether a demo or trial class is available for the Artificial Intelligence course in Nairobi. Our team can confirm the current arrangements and provide details about any available introductory session before you register.
If you miss a live online session, contact the DataMites team to confirm whether a recording, alternative batch, or another way to review the missed topic is available. The options may depend on the course format and batch arrangements, so it is best to check before enrolling.
The specific tools and technologies covered depend on the current course curriculum. Contact DataMites for the latest syllabus, including details of the AI concepts, programming tools, machine learning techniques, and practical exercises included in the Artificial Intelligence course.
DataMites accepts online debit and credit card payments through Razorpay. Credit card EMI may be available, subject to applicable terms; a token advance is collected during registration, and the remaining course fee must be paid in full before course completion.
The DataMites Flexi Pass allows learners to attend multiple batches of the same course during its validity period, making it easier to revisit topics and strengthen their understanding. The pass remains valid for 3 months from the date of activation, subject to the applicable course and batch arrangements.
Refund eligibility, cancellation procedures, and any applicable conditions should be confirmed directly with the DataMites team before you enroll. Please review the current refund policy and request clarification about your circumstances rather than assuming that a particular refund or cancellation condition applies.
DataMites provides a structured artificial intelligence learning program with a stated duration of 9 months and 780+ learning hours. Learners can explore the course curriculum, understand the applicable IABAC certification requirements, and inquire about available learning options to determine whether the program matches their learning goals.
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: -
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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