Data Science And Data Analytics Program
Build Your Data Science and Analytics Career with Our 100% Job Assurance Program
- For fresh graduates/Early career professionals
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6 Months Program
300+ Learning hours
25+ Projects
10+ Tools
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1000
student placed
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18.5LPA
Highest Salary
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52%
Average Salary Hike
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100+
Hiring Partners
1000
Student Placed .
18.5
Highest Salary
52%
Average Salary Hike
100+
Hiring Patners
Our Alumni Work At
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Join The Best Data Science And Analytics Course
Learn the real-world application of data science and build analytical models that enhance business outcomes. This 100% Job Assurance program is ideal for recent graduates and professionals who want to develop a successful data science and analytics career. You will gain practical knowledge about the implications of data science and analytics in real-world businesses and prepare to work as a data science professional in an emerging field of data science and analytics.
100% Job Assurance
Our program comes with a job assurance that offers you 10 guaranteed interviews at over 500 top-tier partner organisations hiring data science and analytics professionals.
Real-world Projects
Implement what you’ve learned with over 25 real-world projects and case studies specially formulated by industry experts to make you job-ready.
Job-specific Curriculum
Learn the practical applications of data science, Python, SQL, data analytics, power BI, and Tableau while gaining expertise in these subjects.
Dedicated Career Services
Our career services include resume development, profile enhancement, career mentorship, interview preparation workshops and one-on-one career counselling to ensure you land the right job.
Live Learning Module
Our expert faculty delivers our robust curriculum using an interactive module and hands-on training methods to prepare you to work in various data science roles.
Expert-Led Instruction
Learn from leading data scientists and industry professionals in an immersive educational environment.
100% Job Assurance
Our program comes with a job assurance that offers you 10 guaranteed interviews at over 500 top-tier partner organisations hiring data science and analytics professionals.
Real-world Projects
Implement what you’ve learned with over 25 real-world projects and case studies specially formulated by industry experts to make you job-ready.
Job-specific Curriculum
Learn the practical applications of data science, Python, SQL, data analytics, power BI, and Tableau while gaining expertise in these subjects.
Dedicated Career Services
Our career services include resume development, profile enhancement, career mentorship, interview preparation workshops and one-on-one career counselling to ensure you land the right job.
Live Learning Module
Our expert faculty delivers our robust curriculum using an interactive module and hands-on training methods to prepare you to work in various data science roles.
Industry
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Evolving Trends in the Data Science World
Using cloud computing for data handling, ML and AL for fraud detection are among current trends in Delhi. Ethical use of technology is among the top priorities, and many initiatives are taken to implement this. The total data availability of Delhi is around 90 MW!
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Delve into the job opportunities in the Data Science domain
Delhi accounts for 20% of India's job vacancies in Data Science and Analytics. There is a significant demand for proficient professionals who possess expertise in tech tools like Spark, Python, SQL, Tableau, and Power BI.
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Pay scale in the Data Science and Analytics industry
The capital of India is ahead of every city in the Data Science industry with an average salary of Rs. 108,000 per month. A person with under two years of experience can earn up to Rs. 60,000 per month while a professional with 3-6 years of experience can get around RS. 100,000.
Curriculum
Our leading edge curriculum covers fundamentals and more complex data science and analytics concepts.
Common Curriculum
Explore key topics in Data Analytics and Data Science with our integrated curriculum—essential for success in both fields.
Excel
What Will You Achieve?
Build a strong foundation of Excel for data analysis Summaries data with pivot tables and chartsTopics
Working with Data
Data types and best practices for data entry Using ranges, tables, and data validation Understanding date and time functions Conditional functions like IF, COUNTIF, SUMIFMastering Excel Functions
Exploring logical functions: AND, OR, NOT Mastering lookup functions: VLOOKUP, HLOOKUP, INDEX, MATCH Nesting functions for complex calculations Text functions to manipulate stringsAdvanced Data Analysis Tools
Exploring What-If Analysis tools: Data Tables, Scenario Manager, Goal Seek Solving complex problems with Solver Introduction to array formulas for complex calculationsData Visualization
Creating and customizing charts Using conditional formatting to highlight data Introduction to PivotTables for summarizing data PivotCharts and slicers for interactive reportsIntroduction to Macros and VBA
Recording and running macros Writing simple VBA scripts to automate repetitive tasks Customizing the Excel environment with VBAIntegration and Power Tools
Linking Excel with other Office applications Using Power Query to import and transform data Overview of Power Pivot for data modeling An introduction to dashboard creationCapstone Project
Using Excel as part of a data analysis project Integrating knowledge from Python and Excel to analyze a dataset Presenting insights and telling stories with dataSQL
Introduction to SQL and Database Concepts
Working with SQL Joins and Aggregations
Understanding different types of joins: INNER, LEFT, RIGHT, and FULL Using aggregate functions like COUNT, SUM, AVG, MIN, and MAX Grouping data with GROUP BY Filtering grouped data using HAVINGAdvanced SQL Operations
Subqueries: using subqueries in SELECT, FROM, and WHERE clauses Common Table Expressions (CTEs) and WITH clause Advanced data manipulation with INSERT, UPDATE, DELETE, and MERGEMastering SQL Functions and Complex Queries
String functions, date functions, and number functions Conditional logic in SQL with CASE statements Advanced use of data types and castingExploring SQL Window Functions
Introduction to window functions Using OVER() with PARTITION BY, ORDER BY Functions like ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), LAG()SQL Performance Tuning
Understanding indexes, including when and how to use them Query optimization techniques Using EXPLAIN plans to analyze query performanceTransaction Management and Security
Understanding transactions, ACID properties Implementing transaction control with COMMIT, ROLLBACK Basics of database security: permissions, rolesIntegrating SQL with Other Technologies
Linking SQL databases with programming languages like Python Using SQL data in Excel via ODBC, direct queries Introduction to using APIs with SQL databases for web integrationAdvanced Data Analytics Tools in SQL
Using analytical functions for deeper insights Exploring materialized views for performance Dynamic SQL for flexible query generationStatistical Analysis Syllabus for Data Analytics and Machine Learning
Topics
Introduction to Statistics
Overview of Statistics in Data Science Role of statistics in data analysis and machine learning. Basic Statistical Measures Measures of central tendency (mean, median, mode). Measures of dispersion (variance, standard deviation, range, interquartile range).Probability Fundamentals
Probability Concepts Basic probability rules, conditional probability, and Bayes' theorem. Probability Distributions Introduction to normal, binomial, Poisson, and uniform distributions.Hypothesis Testing
Concepts of Hypothesis Testing Null hypothesis, alternative hypothesis, type I and type II errors. Key Tests t-tests, chi-square tests, ANOVA for comparing group means.Regression Analysis
Linear Regression Simple and multiple linear regression analysis. Assumptions of linear regression, interpretation of regression coefficients. Logistic Regression Understanding logistic regression for binary outcomes.Multivariate Statistics
Advanced Regression Techniques Polynomial regression, interaction effects in regression models. Principal Component Analysis (PCA) Reducing dimensionality, interpretation of principal components.Time Series Analysis
Fundamentals of Time Series Analysis Components of time series data, stationarity, seasonality. Time Series Forecasting Models ARIMA models, seasonal decompositions.Bayesian Statistics
Introduction to Bayesian Statistics Bayes' Theorem revisited, prior and posterior distributions. Applied Bayesian Analysis Using Bayesian methods in data analysis and predictionNon-Parametric Methods
Overview of Non-Parametric Statistics When to use non-parametric methods, advantages over parametric tests. Key Non-Parametric Tests Mann-Whitney U test, Kruskal-Wallis test, Spearman's rank correlation.EDA
Introduction to Pandas
Overview of EDA The importance and objectives of EDA. Key steps in the EDA process.Data Handling with Pandas
Getting Started with Pandas Introduction to Pandas DataFrames and Series. Data Cleaning Techniques Handling missing values. Data type conversions. Renaming and replacing data. Data Manipulation Filtering, sorting, and grouping data. Merging and concatenating datasets. Advanced operations with group by and aggregation.Numerical Analysis with NumPy
Introduction to NumPy Creating and manipulating arrays. Array indexing and slicing. Statistical Analysis with NumPy Basic statistics: mean, median, mode, standard deviation. Correlations and covariance. Generating random data and sampling.Common Cloud Platforms
Cloud Platforms Overview
Common Cloud Platforms Brief overview of AWS, Azure, and Google Cloud Platform. Key services from these platforms (e.g., AWS EC2, AWS S3, Azure VMs, Google Compute Engine)AWS Core Services
Setting up an AWS account. Introduction to EC2 instances for computing and S3 for storage. Basic operations: launching an instance, storing data.Introduction to Web Scraping
What is Web Scraping? The legal and ethical considerations of scraping data from websites. Common use cases in data analytics and business intelligence.Tools and Techniques
Using Python for Scraping Introduction to BeautifulSoup and requests library. Extracting data from HTML: tags, IDs, classes. Handling Web Data Working with APIs using Python. Cleaning and storing scraped data.GIT
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Introduction to Version Control
Basics of Version ControlPurpose and benefits of using Git.Git Fundamentals
Setup and ConfigurationInstalling Git and configuring user settings.Repository ManagementCreating (git init) and cloning (git clone) repositories.Core Git Commands
Daily OperationsStaging (git add) and committing (git commit) changes.Managing branches (git branch, git checkout).Syncing with remote repositories (git push, git pull).<li
Advanced Git Features
Undoing ChangesUsing git revert and git reset.Resolving Conflicts
Identifying and resolving merge conflicts.Collaborative Features
Workflow ModelsOverview of Git workflows like Git Flow.Pull RequestsCreating, managing, and reviewing pull requests.Specialisation Track
Unlock the full potential of data analytics with specialized mentorship and industry-driven projects tailored to enhance your market readiness and placement prospects
Data Analytics
Python
Introduction to Python
What is Python and why use it?Setting up the Python environmentBasic syntax and execution flowWriting your first Python scriptÂ
Variables and Data Types
Understanding variables and basic data types (integers, floats, strings)Type casting and data type conversionControl Flow
Making decisions with if, elif, and elseLooping with for and whileControlling loop flow with break and continueData Structures (Part 1)
Lists: Creation, indexing, and list operationsTuples: Immutability and tuple operationsData Structures (Part 2)
Sets: Usage and set operationsDictionaries: Key-value pairs, accessing, and manipulating dataFunctions
Defining functions and returning valuesFunction arguments and variable scopeAnonymous functions: Using lambdaFile Handling
Reading from and writing to filesHandling different file types (text, CSV, etc.)Error Handling and Exceptions
Try and except blocksRaising exceptionsUsing finally for cleanup actionsObject-Oriented Programming (OOP)
Classes and objects: The fundamentalsEncapsulation: Private and protected membersInheritance: Deriving classesPolymorphism: Method overridingAdvanced Data Structures
List comprehensions for concise codeExploring the collections module: Counter, defaultdict, OrderedDictDecorators and Context Managers
Creating and applying decoratorsManaging resources with context managers and the with statementConcurrency
Introduction to concurrency with threadingUnderstanding the Global Interpreter Lock (GIL)Basics of asynchronous programming with asyncioGoogle Sheet
Introduction to Google Sheets
Overview of Google Sheets Introduction to the interface and key features. Creating, saving, and sharing spreadsheets. Basic Operations* entering and formatting data. Basic functions: SUM, AVERAGE, MIN, MAX.Working with Data
Data Manipulation Using formulas for basic calculations. Copying, pasting, importing, and exporting data. Cell Referencing and Ranges Relative, absolute, and mixed cell references. Naming ranges for easier formula creation.Data Organization and Analysis
Sorting and Filtering Sorting data alphabetically and numerically. Applying filters to refine data views. Data Validation and Conditional Formatting Creating drop-down lists and input rules. Highlighting data dynamically based on conditions.Advanced Formulas and Functions
Lookup Functions* - VLOOKUP, HLOOKUP, and INDEX/MATCH for data retrieval. Logical Functions* - IF, AND, OR, NOT for executing conditional logic.Data Visualization
Creating Charts and Graphs Types of charts available in Google Sheets. Best practices for data visualization. Advanced Chart Features Customizing axes, legends, and data labels. Dynamic charts with QUERY and data validation techniques.Automation and Scripting
Introduction to Google Apps Script Basics of scripting to automate repetitive tasks. Custom functions and macros. Integration with Google Apps Linking Sheets with other Google services like Google Forms and Google Data Studio.Comprehensive Project
Project Planning and Execution Utilize Google Sheets to manage and analyze a real-world data set. Integrate advanced functions, automation, and visualization techniques learned throughout the course Presentation and Collaboration Collaborate in real-time, using sharing and commenting features effectively. Present findings through Google Sheets, showcasing advanced data manipulation and reporting skills.Panda AI
ChatGPT
Module 1: Fundamentals of ChatGPT
Understanding ChatGPT
How ChatGPT Works Introduction to natural language processing (NLP) and transformers. Overview of the GPT architecture and training methods. Setting Up ChatGPT Accessing ChatGPT via API. Basic configurations and settings for analytics use cases.Using ChatGPT in Data Cleaning
Automating Data Preprocessing Using ChatGPT to identify and correct errors in datasets. Examples of scripting ChatGPT to automate data cleaning tasks. Text Data Manipulation Leveraging ChatGPT for text normalization and extraction. Generating summaries from large text datasets to identify trends and patterns.Advanced Data Analysis
Querying Data with ChatGPT How to use ChatGPT to generate SQL queries. Extracting data insights using conversational AI. Enhancing Data Visualization Integrating ChatGPT with visualization tools (e.g., Tableau, Power BI). Generating narrative descriptions for charts and graphs.Implementing ChatGPT in Real-World Scenarios
Case Studies Examples of businesses effectively using ChatGPT in their data analytics. Discussing successful implementations and measured outcomes. Ethical Considerations and Best Practices Understanding the ethical implications of using AI in data analytics. Best practices for maintaining data privacy and integrity.Basics Of Machine Learning
Introduction to Machine Learning
Machine Learning with Python
Using Scikit-Learn Introduction to the Scikit-Learn library. Building simple models: linear regression and logistic regression. Model Evaluation Splitting data into train and test sets. Understanding key metrics: accuracy, precision, recall, F1 score.Power BI
Power BI Syllabus for Data Analytics
Beginner Level
Week 1:
Introduction to Power BI and Data Visualization Overview of Power BI Introduction to BI and the role of Power BI. Power BI Desktop vs Power BI Service vs Power BI Mobile. Setting up Power BI Environment Downloading and installing Power BI Desktop. Navigating the interface: ribbons, views, and basic configurations.Connecting to Data Sources
Data Importing Techniques Connecting to various data sources: Excel, SQL databases, web data Understanding and utilizing Power BI connectors. Data Preparation Using Power Query for data transformation. Basic data cleaning and transformation tasks.Modeling Data
Creating Data Models Introduction to relationships in Power BI. Building effective data models for analysis. DAX Basics Understanding DAX and its importance. Creating basic calculated columns and measuresAdvanced Data Analysis and DAX
Advanced DAX Functions Writing complex DAX formulas for calculated measures. Time intelligence functions to analyze time-series data. Analytical Techniques Using DAX for advanced data manipulation. Scenario analysis and forecasting.Advanced Visualization Techniques
- Complex Visualizations Creating custom visuals with Power BI. Integrating R and Python visuals into Power BI reports. Performance Optimization Techniques to enhance the performance of Power BI reports. - Managing and optimizing data refreshes.Administration and Security in Power BI
Power BI Service Administration Administering workspaces, datasets, and reports. Setting up data gateways. Security and Compliance Implementing row-level security. Compliance features within Power BI.Integration with Other Technologies
Integrating Power BI with Other Tools Using Power BI with cloud services like Azure. Automating workflows with Power Automate.Advanced Data Analytics Tools in Power BI
AI Insights Utilizing AI features in Power BI for predictive analytics. Advanced analytics using Azure Cognitive Services.Capstone Project
*Real-World Business Analytics Project Applying all learned skills to a comprehensive business intelligence project. Designing, building, and deploying a full Power BI solution. Presenting actionable insights and strategic recommendations based on data.Data Science Machine Learning
Advance your Data Science and Machine Learning expertise with dedicated mentorship and real-world projects, strategically designed to boost your career and placement opportunities.
Python
Introduction to Python
What is Python and why use it? Setting up the Python environment Basic syntax and execution flow Writing your first Python scriptVariables and Data Types
Understanding variables and basic data types (integers, floats, strings) Type casting and data type conversionControl Flow
Making decisions with if, elif, and else Looping with for and while Controlling loop flow with break and continueData Structures (Part 1)
Lists: Creation, indexing, and list operations Tuples: Immutability and tuple operationsData Structures (Part 2)
Sets: Usage and set operations Dictionaries: Key-value pairs, accessing, and manipulating dataFunctions
Defining functions and returning values Function arguments and variable scope Anonymous functions: Using lambdaFile Handling
Reading from and writing to files Handling different file types (text, CSV, etc.)Error Handling and Exceptions
Try and except blocks Raising exceptions Using finally for cleanup actionsObject-Oriented Programming (OOP)
Classes and objects: The fundamentals Encapsulation: Private and protected members Inheritance: Deriving classes Polymorphism: Method overridingAdvanced Data Structures
List comprehensions for concise code Exploring the collections module: Counter, defaultdict, OrderedDictDecorators and Context Managers
Creating and applying decorators Managing resources with context managers and the with statementConcurrency
Introduction to concurrency with threading Understanding the Global Interpreter Lock (GIL) Basics of asynchronous programming with asyncioMongoDB
Introduction to NoSQL and MongoDB
Week 1: Understanding NoSQLOverview of NoSQL
Definition and evolution of NoSQL databases. Differences between NoSQL and traditional relational database systems (RDBMS). Types of NoSQL Databases Key-value stores, document stores, column stores, and graph databases. Use cases and examples of each type.Week 2: NoSQL Concepts and Data Models
NoSQL Data Modeling Understanding NoSQL data modeling techniques. Comparing schema-on-read vs. schema-on-write. Advantages of NoSQL Scalability, flexibility, and performance considerations. When to choose NoSQL over a traditional SQL database.Getting Started with MongoDB
Installing MongoDB Setting up MongoDB on different operating systems. Understanding MongoDB’s architecture: databases, collections, and documents. Basic Operations in MongoDB CRUD (Create, Read, Update, Delete) operations. Using the MongoDB Shell and basic commands.Working with Data in MongoDB
Data Manipulation Inserting, updating, and deleting documents. Querying data: filtering, sorting, and limiting results. Indexing and Aggregation Introduction to indexing for performance improvement. Basic aggregation operations: $sum, $avg, $min, $max, and $group.Machine Learning
Introduction to Machine Learning
Overview of Machine LearningDefinitions and Significance*:
Students will explore the fundamental concepts and various definitions of machine learning, understanding its crucial role in leveraging big data in numerous industries such as finance, healthcare, and more.Types of Machine Learning:
The course will differentiate between the three main types of machine learning: supervised learning (where the model is trained on labeled data), unsupervised learning (where the model finds patterns in unlabeled data), and reinforcement learning (where an agent learns to behave in an environment by performing actions and receiving rewards).Supervised Learning Algorithms
Regression Algorithms
Linear Regression*: Focuses on predicting a continuous variable using a linear relationship formed from the input variables. Polynomial Regression: Extends linear regression to model non-linear relationships between the independent and dependent variables. Decision Tree Regression: Uses decision trees to model the regression, helpful in capturing non-linear patterns with a tree structure.Classification Algorithms
Logistic Regression: Used for binary classification tasks; extends to multiclass classification under certain methods like one-vs-rest (OvR). K-Nearest Neighbors (KNN): A non-parametric method used for classification and regression; in classification, the output is a class membership. Support Vector Machines (SVM): Effective in high-dimensional spaces and ideal for complex datasets with clear margin of separation. Decision Trees and Random Forest*: Decision Trees are a non-linear predictive model, and Random Forest is an ensemble method of Decision Trees. Naive Bayes: Based on Bayes’ Theorem, it assumes independence between predictors and is particularly suited for large datasets.Ensemble Methods and Handling Imbalanced Data
Ensemble Techniques - Detailed techniques such as Bagging (Bootstrap Aggregating), Boosting, AdaBoost (an adaptive boosting method), and Gradient Boosting will be covered, emphasizing how they reduce variance and bias, and improve predictions. Strategies for Imbalanced Data* - Techniques such as Oversampling, Undersampling, and Synthetic Minority Over-sampling Technique (SMOTE) are discussed to handle imbalanced datasets effectively, ensuring that the minority class in a dataset is well-represented and not overlooked.Unsupervised Learning Algorithms
Clustering Techniques
K-Means Clustering: A method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters. Hierarchical Clustering*: Builds a tree of clusters and is particularly useful for hierarchical data, such as taxonomies. DBSCAN: Density-Based Spatial Clustering of Applications with Noise finds core samples of high density and expands clusters from them. Association Rule Learning - Apriori and Eclat algorithms*: Techniques for mining frequent itemsets and learning association rules. Commonly used in market basket analysis.Model Evaluation and Hyperparameter Tuning
Evaluation Metrics* - Comprehensive exploration of metrics such as Accuracy, Precision, Recall, F1 Score, and ROC-AUC for classification; and MSE, RMSE, and MAE for regression. Hyperparameter Tuning - Techniques such as Grid Search, Random Search, and Bayesian Optimization with tools like Optuna are explained. These methods help in finding the most optimal parameters for machine learning models to improve performance.Basics of Deep Learning
Deep Learning Basics Syllabus
Introduction to Natural Language Toolkit (NLTK)
Getting Started with NLTK Installation and setup of NLTK Overview of NLTK's features and capabilities for processing text. Basic Text Processing with NLTK* Tokenization: Splitting text into sentences and words. Text normalization: Converting text to a standard format (case normalization, removing punctuation). Stopwords removal: Filtering out common words that may not add much meaning to the text.Basics of OpenCV
Introduction to OpenCV Installing and setting up OpenCV. Understanding how OpenCV handles images. Basic Image Processing Techniques Reading, displaying, and writing images. Basic operations on images: resizing, cropping, and rotating. Image transformations: Applying filters and color space conversions.Basics of Convolutional Neural Networks (CNN)
Understanding CNNs The architecture of CNNs: Layers involved (Convolutional layers, Pooling layers, Fully connected layers). The role of Convolutional layers: Feature detection through filters/kernels. Implementing a Simple CNN Building a basic CNN model for image classification. Training a CNN with a small dataset: Understanding the training process, including forward propagation and backpropagation.Basics of Recurrent Neural Networks (RNN)
Introduction to RNNs Why RNNs? Understanding their importance in modeling sequences. Architecture of RNNs: Feedback loops and their role. Challenges with Basic RNNs Exploring issues like vanishing and exploding gradients. Introduction to LSTMs How Long Short-Term Memory (LSTM) networks overcome the challenges of traditional RNNs. Building a simple LSTM for a sequence modeling task such as time series prediction or text generation.Capstone Project
Applying Deep Learning Skills Choose between a natural language processing task using NLTK, an image processing task using OpenCV, or a sequence prediction task using RNN/LSTM. Implement the project using the techniques learned over the course. Presentation of Results Summarize the methodology, challenges faced, and the insights gained. Demonstrate the practical application of deep learning models in solving real-world problems.Introduction to Web Frameworks for Data Science and ML Flask
Introduction to Web Frameworks for Data Science and ML Flask: Basics to Intermediate
Introduction to Flask
Overview of Flask What is Flask? Understanding its microframework structure. Setting up a Flask environment: Installation and basic Configuration First Flask Application Creating a simple app: Routing and view functions. Templating with Jinja2: Basic templates to render data.Flask Routing and Forms
Advanced Routing Dynamic routing and URL building. Handling different HTTP methods: GET and POST requests. Working with Forms Flask-WTF for form handling: Validations and rendering forms. CSRF protection in Flask applications.Flask and Data Handling
Integrating Flask with SQL Databases Using Flask-SQLAlchemy: Basic ORM concepts, creating models, and querying data. API Development with Flask Creating RESTful APIs to interact with machine learning models. Using Flask-RESTful extension for resource-based routes.FastAPI: Basics to IntermediateIntroduction to FastAPI
Why FastAPI? Advantages of FastAPI over other Python web frameworks, especially for async features. Setting up a FastAPI project: Installation and first application. FastAPI Routing and Models Path operations: GET, POST, DELETE, and PUT. Request body and path parameters: Using Pydantic models for data validation.Building APIs with FastAPI
API Operations Advanced model validation techniques and serialization. Dependency injection: Using FastAPI's dependency injection system for better code organization. Asynchronous Features Understanding async and await keywords. Asynchronous SQL database interactions using databases like SQLAlchemy async.Serving Machine Learning Models
Integrating ML Models Building endpoints to serve predictions from pre-trained machine learning models. Handling asynchronous tasks within FastAPI to manage long-running ML predictions. Security and Production Adding authentication and authorization layers to secure APIs. Tips for deploying Flask and FastAPI applications to production environments.Introduction to Deep Learning Docker For Data Scince & ML
Basic Docker Curriculum for Data Science and Machine Learning
Introduction to Docker
Overview of Docker* Understanding what Docker is and the core concepts behind containers. Differences between Docker and traditional virtualization. Setting up Docker Installing Docker on various operating systems (Windows, macOS, Linux). Navigating Docker interfaces (Docker Desktop, Docker CLI).Docker Basics
Docker Images and Containers Understanding images vs. containers. Managing Docker images—pulling from Docker Hub, exploring Dockerfile basics. Running Containers Starting, stopping, and managing containers. Exposing ports, mounting volumes, and linking containers.Docker Compose and Container Orchestration
Introduction to Docker Compose Benefits of using Docker Compose. Writing a docker-compose.yml file for multi-container applications. Basic Orchestration* - Understanding the need for orchestration. Overview of Docker Swarm mode for managing a cluster of Docker Engines.Docker for Data Science
Creating a Data Science Work Environment Building a custom Docker image for data science environments. Including tools like Jupyter Notebook, RStudio, and popular data science libraries (Pandas, NumPy, Scikit-learn). Data Persistence in Containers Strategies for managing data in Docker, focusing on non-volatile data storage.Advanced Docker Applications in Data Science
Deploying Machine Learning Models Containerizing machine learning models for consistent deployment. Using Docker containers to deploy a model to a production environment. - *Best Practices and Security Understanding Docker security best practices. - Maintaining and updating data science Docker environments.![](https://schoolofcoreai.com/wp-content/uploads/2024/04/9monthst.webp)
Will I Get Certified?
Upon successfully completing this program, you’ll earn a Certificate in Data Science and Analytics . This certification will add considerable value to your professional credentials.
![](https://schoolofcoreai.com/wp-content/uploads/2024/04/certificate-4_page-0001-1024x724.jpg)
Programming Tools and Languages
Offers
Data Science With ML
₹ 45000 ₹ 29999 Online
- Duration : - 6 Months
Data Analytics
₹ 34999 ₹ 24999 Online
- Duration :-5 Months
What do students say about the School of Core AI ?
![](https://schoolofcoreai.com/wp-content/uploads/2024/02/rahul.jpg)
My career started in sales, and I decided to pivot to technology for new challenges. This course prepared me excellently for the world of data analysis. I am now successfully employed as a Data Analyst and couldn’t be more satisfied with my career change
![](https://schoolofcoreai.com/wp-content/uploads/2024/02/vipul.jpg)
With a degree in BSc Chemistry, I initially doubted my ability to break into the tech industry. This course was a game changer, transforming my skills and helping me secure a position as a Data Analyst. The quality of teaching and the support from the placement team were beyond my expectations
![](https://schoolofcoreai.com/wp-content/uploads/2024/02/bhupendra.jpg)
was a professional graphic designer and wanted to upgrade my career in tech. This program not only provided me with the necessary skills but also opened doors to a new career as a Data Scientist. Special thanks to the placement team for helping me navigate this transition smoothly
![](https://schoolofcoreai.com/wp-content/uploads/2024/04/WhatsApp-Image-2024-04-18-at-8.21.00-PM.jpeg)
After completing my BA in English, I never imagined I could shift to a tech-focused career. This course not only taught me the essentials of coding but also how to apply these skills in real-world data analytics. I'm now thrilled to be working as a Data Analyst. A huge thank you to the trainers and placement team for their unwavering support
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This course was a game-changer for me. I was not able to secure a placement during college, but the detailed and well-paced curriculum here built up my skills and confidence. Now, I'm working as a Data Engineer, and it's all thanks to the excellent preparation and support from the faculty. Highly recommend it to anyone looking to boost their career in data science!
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The curriculum was nothing short of transformative. I particularly appreciated the hands-on projects that mirrored real-world problems. I entered the course unsure about my coding abilities but left with a job offer as a Junior Data Scientist. I couldn't have asked for a better learning experience!
Other Courses
Data Science with Deep Learning
- Intermediate
- 24 Weeks + Projects
- Placement Guarantee
Skills You Gain
Full Stack Data Science
- Intermediate
- 35 Weeks + Projects
- Placement Guarantee
Skills You Gain
FAQ
What will I learn in the Data Science and Analytics courses?
You will gain skills in statistical analysis, machine learning, data manipulation, and visualization. Real-world datasets and case studies will be used to apply these skills effectively to solve business challenges.
Who is eligible to enroll in these courses and are there any prerequisites?
The courses are open to everyone, including recent graduates and professionals looking to transition into data science. No specific educational background is required. A basic understanding of mathematics and an interest in data analysis will be beneficial. Foundational modules are provided to ensure all participants can engage fully with advanced topics.
Do you offer any job placement or career support?
Yes, we offer 100% job placement assistance along with career counseling, interview preparation, and personalized mentoring from industry experts to ensure you are job-ready upon course completion.
How long does it take to complete the courses and what are the scheduling options?
The courses are designed to be completed within 5 to 6 months, with flexible scheduling options including weekday and weekend classes, as well as early morning and late evening sessions to accommodate various professional schedules.
Can I balance the course with a full-time job?
Absolutely! Our courses are flexible and self-paced, allowing you to access materials at any time and progress according to your own schedule, making it possible to balance learning with full-time job commitments.