CoursesData ScienceData Science with AI
CID11015
Data Science with AI
This comprehensive course equips learners with Python programming, NumPy, Pandas, and Matplotlib for data manipulation and visualization. Dive into machine learning, deep learning, NLP, and computer vision. Master Azure for MLOps to deploy and manage ML models. Prepare for a data science career with hands-on projects and real-world skills.
duration
110 Hours
NEXT BATCH
Nov 20, 2024
mode
Online
payment options
Program Overview
Explore data science and AI journey from Python basics to advanced machine learning and deep learning. Analyze data, visualize insights, and build AI models with industry-standard tools.
Master in-demand skills for extracting insights from data and building AI models
Learn to use popular tools and libraries such as Python and Keras
Understand how to apply AI techniques to real-world problems and make data-driven decisions
Open up career opportunities in data science, AI engineering, and related fields
Eligiblility
Basic understanding of mathematics, statistics, and programming. Python familiarity is beneficial but not mandatory. Open to students, professionals, and enthusiasts passionate about data science.
Why should you learn?
Upon completion, learners gain comprehensive Python, NumPy, Pandas, and Matplotlib skills for data manipulation and visualization. Acquire ML, DL, NLP, and CV skills for complex data challenges and data science careers.
Possible Job Roles
Upon course completion, learners can pursue diverse data science roles such as Data Analyst, Data Scientist, Machine Learning Engineer and AI engineer.
Salary Range
4 to 20 Lakhs Per Annum
Course Modules
110 Hours COURSE
6 hours
Python For Data Science
+The "Python" module covers basic programming concepts, string manipulation, data structures, date/time operations, and file handling, suitable for non-IT students.
Introduction to Python Programming
Variables, local and global variables, loops, functions, programming exercises
Python Strings
Operators Lists, Tuples, Dictionary, Date&Time,- Files I/O Exceptions
3 hours
NUMPY (Numerical Python)
+The "NUMPY" module covers ndarray operations, universal functions, statistical functions, indexing, slicing, and advanced operations for numerical computing.
Ndarray Object
Numpy arrays-Basic Operations,Universal Functions
Arange, linspace, logspace Indexing and Slicing Statistical Functions Dot Products
Matrix Multiplication
5 hours
Pandas
+The "Pandas" module covers Series and DataFrames, focusing on creation, manipulation, and essential functionalities for efficient data analysis.
Series
DataFrames
4 hours
Data Visualisation using Mathplotlib
+The "Data Visualization with Matplotlib" module teaches core principles and techniques for creating effective visualizations, progressing to advanced charting.
Principles of Information Visualisation
Basic Charting
Charting Fundamentals
Applied Data Visualizations
5 hours
Machine Learning using sklearn
+In "Machine Learning with sklearn," explore regression, classification, and clustering using various algorithms, enhancing machine learning model building skills.
Regression
Classification
Clustering
4 hours
Deep Learning using TensorFlow and Keras
+Explore advanced neural network concepts, including CNNs for image processing, RNNs for sequence data, and LSTMs for long-term dependencies.
Introduction to Perceptron and Neural Networks (ANN)
CNN
RNN
LSTM
6 hours
NLP using NLTK and Spacy
+Learn NLP fundamentals, including text tokenization, word lemmatization, semantics with word vectors, stop words, and sentiment analysis.
NLP Introduction and Basics
Tokenization
Lemmatization
Semantics and word vectors
Stop words
Sentimental Analysis
5 hours
Computer Vision using cv2
+Explore image processing and computer vision essentials, including image/video manipulation, color/shape/face detection, and number plate extraction using OpenCV.
Image processing and Computer Vision basics
Loading Images and Videos
Color and Shape detection
Face detection
Number plate detection
3 hours
Generative AI for Data Science
+Explore Prompt Engineering techniques and integrate Generative AI into the data science life cycle for data generation and analysis.
Introduction to Gen AI and LLM
Prompt Engineering
Data Science life cycle using Gen AI
5 hours
Azure for Data Science with MLOPs
+Discover Azure tools for training ML models, orchestrating pipelines, deploying batch inference pipelines, and monitoring performance for efficient ML solutions.
Training ML models in Azure
Automating ML models
Orchestrating Pipelines
Deploying batch inference pipelines
Monitoring Performance
Certification of Completion
Certifies completion of data science course, covering fundamentals, machine learning, data analysis, and visualization.
Admission Process at Cokonet
The course admission process at Cokonet involves streamlined procedures ensuring efficient enrollment for prospective students.
Step 1Speak With our Career Counselor
Our career counselor will help you identify the suitable course for you.
Step 2Complete Payment
Finalize the transaction securely, using the provided payment methods.
Step 3Get Enrolled
Enroll in the chosen course, providing personal details and payment information
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Financing & Support
0% Interest Loans
Access 0% interest loans (6/9/12 Months EMI) for your education, ensuring affordability while you pursue your dreams.
Pay in Installments
Ease your financial burden with our convenient installment payment options.
Scholarships
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Laptop Support
Seamless assistance with our comprehensive laptop support services.
Data Science with AI: Frequently Asked Questions (FAQs)
Concerns regarding our Data Science course? Find the answers to the questions you often ask
Basic knowledge of mathematics, statistics, and programming concepts is recommended but not mandatory.
Familiarity with Python or R is beneficial but not mandatory. The course covers these languages as part of the curriculum.
This course prepares you for various roles in data science and AI, including data scientist, machine learning engineer, AI developer, and more.
Yes, you will need access to Python, NumPy, Pandas, Matplotlib, and possibly Azure for MLOps, which will be specified at the beginning of the course.
