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Master Diploma in Data Science & Artificial Intelligence
Learn about programming languages and machine learning algorithms with the Master Diploma in Data Science & Artificial Intelligence certification course.
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 knowledge and skills necessary to excel in the rapidly evolving fields of data science and artificial intelligence. This course by Boston Institute of Analytics, Banjarahills covers a wide range of topics, including machine learning, statistical analysis, data mining, and predictive modelling, providing students with a solid foundation in the fundamental concepts and techniques essential for extracting valuable insights from complex datasets.
The Master Diploma in Data Science & Artificial Intelligence training covers topics such as machine learning, statistical analysis, data mining, and predictive modelling, providing students with a solid foundation in the fundamental concepts and techniques essential for extracting valuable insights from complex datasets. Throughout the Master Diploma in Data Science & Artificial Intelligence Certification by Boston Institute of Analytics, Banjarahills students will engage in hands-on projects and practical exercises, allowing them to apply theoretical knowledge to real-world scenarios.
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The highlights
- Offered by Boston Institute of Analytics
- Completion Certificate
- 10 months certification programme
- 6 months on-job training as Data Scientist
- Hands-on learning experience
Program offerings
- Course readings
- Practical learning
Course and certificate fees
certificate availability
certificate providing authority
Eligibility criteria
Certification Qualifying Details
To qualify for the Master Diploma Data Science & Artificial Intelligence course by Boston Institute of Analytics, Banjarahills, candidates are required to complete the full course and the projects.
What you will learn
With the Master Diploma in Data Science & Artificial Intelligence certification syllabus, students will emerge with a profound understanding of the intricacies of both data science and artificial intelligence. The course equips them with a diverse skill set, including expertise in machine learning algorithms, statistical analysis, data preprocessing, and predictive modelling.
Upon completion of the Master Diploma in Data Science & Artificial Intelligence certification course, students will have hands-on experience applying these concepts to real-world problems through practical projects, honing their ability to extract meaningful insights from complex datasets.
Who it is for
The Diploma in Data Science & Artificial Intelligence certification course is designed for Data Scientists, Data Engineers, and Machine Learning Engineers to gain emphasis on real-world applications and ensures that students can seamlessly integrate their newfound knowledge into their professional roles.
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/banjarahills/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: They would be contacted after that to receive additional information regarding the course.
Step 4: Thereafter, they are required to pay the course fee in the payment gateway option.
Step 5: 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.
Evaluation process
Candidates for the Master Diploma in Data Science & Artificial Intelligence certification course are required to take the examination online in order to receive the certification programme.
How it helps
The Master Diploma in Data Science & Artificial Intelligence Certification benefits the student by providing a comprehensive and up-to-date curriculum that covers the essential facets of both data science and artificial intelligence, ensuring that students possess a well-rounded skill set aligned with industry demands. The hands-on projects embedded in the course enable the practical application of theoretical knowledge, fostering a deep understanding of concepts and methodologies.
FAQs
The duration of this certification course is 10 months and thus candidates can join the course at their own convenience.
Candidates can submit the payment via different DBT and UPI methods.
The course enhances job market competitiveness by certifying students' proficiency in advanced technologies and techniques, making them attractive candidates for roles in data science, machine learning, and artificial intelligence.
The certification programme consists of practical and hands-on learning experiences. Candidates also receive course readings for study purposes.
Students can receive a completion certificate after completing the full course and the projects.