4.9 (22,300) reviews ETB 126,580
ETB 92,192*
*Offer valid till 31st October 2026
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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!
Eligibility for an artificial intelligence course in Ethiopia depends on the course level and curriculum. Beginners can usually start with basic computer skills and an interest in problem-solving, while advanced programs may require mathematics, statistics, or programming knowledge.
An online AI course in Ethiopia can suit university students, recent graduates, working professionals, software developers, business analysts, and individuals planning a career change. Most introductory courses welcome beginners, while specialized programs may require prior knowledge of programming or mathematics.
The duration of an online artificial intelligence course in Ethiopia varies according to its depth and learning format:
Self-paced courses may take longer or shorter depending on the learner's schedule and practical project requirements.
As a general budgeting estimate, short online AI courses may cost around ETB 1,000 to 10,000, while comprehensive professional programs may range from ETB 15,000 to 80,000 or more. These are indicative estimates rather than verified market averages, and actual fees depend on course duration, curriculum, practical projects, instructor support, and certification.
Completing an AI course can help learners prepare for roles that involve developing intelligent software, analyzing data, or automating business tasks. Potential career options include:
Entry requirements vary, and practical projects, technical skills, and relevant experience can improve job readiness.
According to OptiSalary's estimate, an AI professional working as an AI engineer in Ethiopia earns approximately ETB 23,709 per month before tax, equivalent to ETB 284,506 annually. This estimate is based on 14 salary submissions and may vary according to experience, technical skills, employer, and job responsibilities.
Yes, people from business, finance, marketing, healthcare, and other non-technical backgrounds can work towards AI-related careers in Ethiopia. Starting with AI fundamentals, basic statistics, and beginner-friendly Python before progressing to practical projects can help build the required technical foundation.
Python is widely used in AI because it offers accessible tools for data analysis, machine learning, and deep learning. Other useful languages include
The best language to learn first depends on the learner's goals, although Python is a practical starting point for most beginners.
Artificial intelligence can be a worthwhile career choice in Ethiopia for learners interested in technology, data, and solving practical problems. Potential applications include agriculture, healthcare, financial services, education, and digital services, although employment opportunities depend on employer needs, available infrastructure, and the individual's skills.
An online artificial intelligence course in Ethiopia may help learners develop technical and analytical skills through lessons, exercises, and projects. Depending on the syllabus, these skills can include Python programming, data preparation, statistics, machine learning, deep learning, generative AI tools, model evaluation, and responsible AI practices.
Common AI-related career paths include machine learning engineer, data scientist, AI software developer, data analyst, and natural language processing specialist. Some positions focus on building predictive models, while others involve preparing data, developing applications, or integrating AI tools into existing business systems.
No, prior coding knowledge is not essential for an introductory artificial intelligence course in Ethiopia. Beginners can learn programming fundamentals and Python before progressing to algorithms, machine learning models, and more advanced AI applications.
Generative AI tools can help Ethiopian businesses and organizations draft documents, summarize information, translate content, support customer service, and assist with software development. Their usefulness depends on data quality, language support, internet access, and human review to identify errors and protect confidential information.
Artificial intelligence (AI) is the broad field of creating computer systems that perform tasks requiring human-like capabilities, such as reasoning or understanding language. 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 multi-layered neural networks to handle complex tasks such as image recognition and speech processing.
Emerging areas of interest in Ethiopia include AI applications for agriculture, healthcare, financial services, education, local-language technologies, and public services. Other important developments include generative AI, automation, data governance, and efforts to strengthen local AI skills and computing infrastructure.
Skills that may be useful as Ethiopian organizations adopt more AI solutions include Python programming, data analysis, machine learning, generative AI application development, and cloud-based deployment. Knowledge of local-language processing, data privacy, cybersecurity, and responsible AI can also be valuable, depending on the industry and role.
Start by learning AI fundamentals, basic statistics, and Python, then practice working with datasets and building simple machine learning projects. Create a portfolio that demonstrates your skills, learn how to explain your results, and apply for suitable internships, junior data roles, software development positions, or entry-level AI opportunities.
Demand for AI professionals in Ethiopia is developing as organizations explore automation, data-driven decision-making, and AI applications across different industries. However, reliable nationwide figures showing the number of AI vacancies or the overall shortage of AI professionals are not established here, so the strength of demand should be assessed through current job listings, employer requirements, and opportunities in specific sectors.
The DataMites AI Engineer course is suitable for learners interested in developing skills in artificial intelligence and related technologies. If you are a beginner or have a background in programming, mathematics, IT, or a related field, contact the DataMites team to confirm the recommended prerequisites for your learning goals.
The fee for the DataMites Artificial Intelligence course in Ethiopia ranges from ETB 75,620 to ETB 126,580, depending on the applicable course option. Contact the DataMites team to confirm the current fee and available payment options before enrolling.
The DataMites AI Engineer course runs for 9 months with 780+ learning hours. This duration allows learners to build their understanding of AI concepts and technologies through a structured learning schedule.
Visit the official DataMites website and navigate to the Artificial Intelligence course page to review the available course details. You can contact the DataMites team to confirm enrollment requirements, upcoming batch information, and payment procedures before completing your registration.
For information about the current delivery format, including live online classes, contact the DataMites team before registering. They can confirm the available learning arrangements and explain how the course is delivered to learners in Ethiopia.
After completing the applicable course requirements, learners can receive IABAC certification. NASSCOM FutureSkills Certification is also available for eligible NRI learners, subject to the applicable requirements; contact the DataMites team to confirm the certification options for your enrollment.
DataMites accepts online debit and credit card payments through Razorpay, and credit card EMI may be available. A token advance is collected during registration, and the remaining course fee must be paid in full before course completion; contact the team to confirm the payment options available for your location.
You can contact the DataMites team to ask whether a demo or trial class is available for the Artificial Intelligence course. They can confirm the current arrangements and any applicable conditions before you register.
If you miss a live online class, contact the DataMites team to confirm whether a recording, alternative session, or other learning resource is available. Access to missed-class materials may depend on the course arrangements, so it is best to clarify this before enrolling.
The course's specific internship arrangements and practical learning components should be confirmed with the DataMites team before enrollment. Ask the team whether projects, exercises, or internship opportunities are included in the particular AI Engineer course option you are considering.
The DataMites Artificial Intelligence course may suit graduates, freshers, working professionals, and career switchers who want to develop AI knowledge. It can also be relevant to learners with programming, mathematics, data analysis, or IT experience who want to explore AI engineering.
An AI engineer curriculum may cover topics such as machine learning, deep learning, neural networks, Python programming, and AI model development. For the exact subjects, tools, technologies, and project details included in the current DataMites AI Engineer course, contact the team or review the latest syllabus before enrolling.
The course fee ranges from ETB 75,620 to ETB 126,580. To understand exactly what the selected fee includes, such as learning materials, certification-related charges, or other course resources, contact the DataMites team for a detailed fee breakdown.
DataMites offers a Flexi Pass that allows learners to attend multiple batches of the same course during its validity period, making it easier to revisit topics when needed. The Flexi Pass remains valid for 3 months from the date of activation; contact the team to confirm its applicability to your selected AI course.
Refund eligibility and cancellation terms depend on the applicable DataMites policy and enrollment conditions. Please contact the DataMites team before cancelling to confirm the current refund policy, any applicable conditions, and the procedure for submitting a request.
DataMites provides a structured AI Engineer course designed to help learners develop their understanding of artificial intelligence over 9 months with 780+ learning hours. Learners can also explore IABAC certification, applicable NASSCOM FutureSkills Certification options for eligible NRI learners, and the Flexi Pass for revisiting the same course through multiple batches during its validity period.
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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