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Master Diploma in Data Science and Artificial Intelligence
Learn about data science & artificial intelligence with Master Diploma in Data Science & Artificial Intelligence programme by the Boston Institute of Analytics.
Part time, Online
10 Months
Quick facts
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Medium of instructions
English
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Mode of learning
Virtual Classroom, Campus Based/Physical Classroom
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Mode of Delivery
Video and Text Based
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Course overview
The Master Diploma in Data Science & Artificial Intelligence is a certification course designed to equip students with the skills and knowledge necessary to thrive in the dynamic fields of data science and artificial intelligence. This course explores the foundational concepts of data analysis, machine learning, and statistical modelling, providing a detailed understanding of the methodologies and tools involved in data science and artificial intelligence.
The Master Diploma in Data Science & Artificial Intelligence Certification by the Boston Institute of Analytics provides students with practical experience in data manipulation, feature engineering, and model deployment. The course also encompasses cutting-edge topics in artificial intelligence, data science exploring neural networks, deep learning, natural language processing, and computer vision.
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The highlights
- 10 months certification course
- Certification from the Boston Institute of Analytics
- Two-in-One Certification
- Classroom + Online Mode
- 6 months of on Job Training
- Interactive classes
- Hands-on learning experience
Program offerings
- Course readings
- Practical learning
Course and certificate fees
certificate availability
certificate providing authority
Eligibility criteria
Academic Qualifications
Candidates from all academic streams such as arts, science, commerce, and engineering can apply for this course.
Certification Qualifying Details
To qualify for the Master Diploma in Data Science & Artificial Intelligence training, candidates are required to complete the full course.
What you will learn
The Master Diploma in Data Science & Artificial Intelligence certification course provides students with a profound skill set and a comprehensive understanding of key concepts in these dynamic fields. Students will master the art of data analysis, possessing the ability to manipulate and derive insights from complex datasets.
Upon completion of the Master Diploma in Data Science & Artificial Intelligence certification course, students will gain expertise in artificial intelligence, exploring advanced topics such as neural networks, deep learning, natural language processing, and computer vision. The course emphasis on hands-on projects ensures that students gain practical experience in data manipulation, feature engineering, and model deployment.
Who it is for
The Master Diploma in Data Science & Artificial Intelligence certification course is designed for Data Analysts, Machine Learning Engineers, and Data Scientists and facilitates networking opportunities with industry professionals, fostering connections that can enhance career prospects. Additionally, the course adds credibility to students' skill sets, opening doors to a wide range of career opportunities in data-driven and technology-centric sectors.
Admission details
To join the Master Diploma in Data Science & Artificial Intelligence classes, candidates need to follow these steps:
Step 1: Browse the official URL: https://www.bostoninstituteofanalytics.org/data-science-and-artificial-intelligence
Step 2: Candidates are required to submit the online application by filling out all the necessary and relevant information such as primary email address, name, phone number, and motivation letter.
Step 3: Thereafter, they are required to pay the course fee in the payment gateway option.
Step 4: Candidates would then have to access the course and start the learning process.
Filling the form
To enrol for the Master Diploma in Data Science & Artificial Intelligence training, candidates are required to submit the online application which asks for details such as primary email address, name, phone number and a motivation letter.
The syllabus
Induction
Advanced Excel
- Pivot Tables For Data Summarization
- Data Analysis And Visualization
- Data Linking For Comprehensive Reports
- Practical Exercise: Create A Pivot Table And Build Advanced Charts To Analyze Data From Different Angles.
Loops & Functions In Python
- Iterate With Loops In Python
- Create And Use Python Functions
- Advanced Data Manipulation With Lambda
- Practical Exercise: Implement Loops And Functions To Perform Tasks Such As Data Processing And Automation.
Data Manipulation With Pandas & Data Visualization
- Master Pandas Data Structures
- Explore Data And Visualize Insights
- Introduction To Version Control With Git
- Practical Exercise: Load And Explore A Dataset Using Pandas, And Create Basic Data Visualizations Using Matplotlib And Seaborn.
Advanced Sql Concept & Data Manipulation
- Temporary Tables And Documentation
- Aggregations And Grouping Data
- Advanced Sql Operations And Joins
- Practical Exercise: Perform More Complex Sql Operations, Such As Joining And Aggregating Data.
Advanced Statistics & Hypothesis Testing
- Hypothesis Testing Techniques
- Interpret Data Visualizations
- Correlation, Regression, And Anova
- Practical Exercise: Perform Hypothesis Tests And Analyze Real Datasets Using Statistical Techniques
Multiple Linear Regression & Model Evaluation
- Evaluate Models With Mae, Mse, Rmse
- Multiple Linear Regression
- Practical Model Evaluation With Real Data
- Practical Exercise: Build And Evaluate A Multiple Linear Regression Model Using A Real-World Dataset.
Decision Trees And Ensemble Methods
- Understand Decision Trees
- Prevent Overfitting And Tree Pruning
- Explore Random Forest And Gradient Boosting
- Practical Exercise: Create Decision Tree Models And Explore The Power Of Ensemble Methods.
Unsupervised Learning
- Discover K-Means Clustering
- Hierarchical Clustering Techniques
- Clustering For Data Insights
- Practical Exercise: Implement K-Means Clustering And Hierarchical Clustering On Real Data.
Support Vector Machines (Svm) And K-Nearest Neighbors (Knn)
- Classification With Svm
- K-Nearest Neighbors For Predictions
- Choose K And Distances
- Practical Exercise: Build And Evaluate Svm And Knn Models For Classifcation Problems.
Time Series Modeling With Arima And Sarima
- Understand Time Series Data
- Build Arima And Sarima Models
- Practical Forecasting And Model Evaluation
- Practical Exercise: Analyze And Forecast Time Series Data Using Arima And Sarima Models.
Deep Learning Architectures And Training
- Dive Into Cnns And Rnns
- Train Deep Learning Models
- Avoid Overfitting With Regularization
- Practical Exercise: Create And Train Convolutional Neural Networks (Cnns) And Recurrent Neural Networks (Rnns) For Various Tasks.
Model Deployment
- Understand Model Deployment
- Set Up Deployment Environment
- Secure, Monitor, And Optimize Deployed Models
- Practical Exercise: Deploy A Machine Learning Model As A Web Api And Monitor Its Performance.
Text Generation With Recurrent Neural Networks (Rnns)
- Introduction To Text Generation
- Best Practices To Review Creative Text Generation
- Common Issues In Training Rnns
- Practical Exercise: Building A Text Generator Using Rnns.
Power BI
- Introduction To Power BI
- Data Transformation And Modeling
- Create Interactive Dashboards
- Practical Exercise: Transform Data And Create Interactive Dashboards In Power Bi Using Real-World Datasets.
Introduction To R
- Get Started With R
- Work With Variables And Data Types
- Handle Data Frames And Apply Functions
- Practical Exercise: Perform Data Manipulation And Analysis In R, Including Creating Custom Functions.
Fundamentals Of Excel
- Master Data Cleaning Techniques
- Visualize Data With Excel Charts
- Efficient Subtotaling And Analysis
- Practical Exercise: Clean And Analyze A Provided Dataset Using Excel's Basic Functions And Charts.
Python Fundamentals
- Python Basics And Operators
- Control Flow With Conditional Statements
- Python Data Types And Structures
- Practical Exercise: Write Python Code To Solve Simple Programming Problems, Focusing On Variables And Operators
Numpy Fundamentals
- Work With Numpy Arrays
- Efficient Indexing And Slicing
- Filtering And Boolean Indexing
- Practical Exercise: Work With Numpy Arrays To Perform Basic Array Operations, Indexing, And Filtering.
Introduction To Sql & Basic Querying
- Sql For Data Retrieval
- Data Modeling Fundamentals
- Advanced Data Sorting And Filtering
- Practical Exercise: Write Sql Queries To Retrieve And Manipulate Data From A Sample Database.
Fundamentals Of Statistics & Probability
- Understand Data Types
- Central Tendency And Variance
- Probability And Distribution Basics
- Practical Exercise: Calculate Mean, Median, Variance, And Standard Deviation For A Dataset.
Introduction To Machine Learning And Regression Basics
- Dive Into Machine Learning
- Data Preprocessing Essentials
- Linear Regression For Predictive Modeling
- Practical Exercise: Implement A Simple Linear Regression Model And Evaluate Its Performance.
Logistic Regression And Classification Metrics
- Master Logistic Regression
- Classification Metrics For Model Assessment
- Roc Curves And Model Performance
- Practical Exercise: Train And Evaluate Logistic Regression Models For Binary And Multiclassclassification Problems.
Model Evaluation And Validation Techniques
- K-Fold Cross-Validation
- Hyperparameter Tuning
- In-Depth Classification Metrics
- Practical Exercise: Apply K-Fold Cross-Validation And Hyperparameter Tuning To Improve Model Performance.
Dimensionality Reduction And Feature Selection
- Reduce Dimensionality Effectively
- Principal Component Analysis (Pca)
- Feature Engineering For Improved Models
- Practical Exercise: Apply Pca For Dimensionality Reduction And Feature Engineering To Enhance Model Performance.
Advanced Ensemble Learning
- Bagging, Stacking, And Blending
- Explore Advanced Ensemble Algorithms
- Harness The Power Of Xgboost And Lightgbm
- Practical Exercise: Implement Bagging, Stacking, And Advanced Ensemble Algorithms Like Xgboost And Lightgbm On A Dataset.
Introduction To Deep Learning
- Overview Of Artificial Neural Networks
- Basic Deep Learning Concepts
- Build And Train Simple Neural Networks
- Practical Exercise: Build And Train A Simple Neural Network On A Dataset Using Popular Deeplearning Frameworks.
Natural Language Processing (Nlp)
- Master Nlp Essentials
- Preprocess Text Data
- Create Text Classification Models
- Practical Exercise: Perform Text Preprocessing And Build A Text Classification Model Using Nlp Techniques.
Introduction To Generative Ai
- Types Of Generative Models
- Understanding Generative Adversarial Networks (Gans)
- Understanding Variational Autoencoders (Vae)
- Practical Exercise: Setting-Up Python Environment And Deep Learning Libraries.
Introduction To Transformers
- Rnn Vs Transformer Models
- Overview Of Gpt-2 And Bert
- Nlp Applications And Text Generation With Transformers
- Practical Exercise: Build A Language Model Using Gpt-2.
Tableau
- Explore Tableau Prep And Desktop
- Visual Analytics And Calculations
- Design Engaging Dashboards
- Practical Exercise: Develop Visualizations And Dashboards In Tableau Based On Provided Data
Advanced R Programming
- Data Frames And Custom Functions
- Master Apply Functions
- Work With Dates And Times In R
- Practical Exercise: Utilize Apply Functions, Handle Dates And Times, And Work With Data Frames In R.
How it helps
The Master Diploma in Data Science & Artificial Intelligence certification benefits the student by providing a deep and comprehensive understanding of both data science and artificial intelligence, ensuring that students are equipped with a holistic skill set that spans from data analysis to advanced machine learning and AI techniques. The hands-on course embedded in the curriculum fosters practical experience, enabling students to apply their knowledge to real-world scenarios.
FAQs
Candidates need to submit an online application form by submitting their official details and pay the course fee in the payment gateway option.
The certification programme is 10 months in duration with access to course readings and hands-on learning experience.
Candidates are not provided with any scholarship options for this certification programme.
This course is designed for data scientists, data analysts and machine learning engineers to help students understand the fundamentals involved in data science and artificial intelligence.
Candidates do not need any prerequisites for this certification programme.