Our data science course is designed to equip aspiring professionals with the necessary skills and knowledge to excel in the dynamic field of data science. With a focus on practical applications and hands-on experience, this course will empower you to harness the power of data and drive impactful insights for businesses across industries.
KEY HIGHLIGHTS OF DATA SCIENCE
Comprehensive Curriculum
Our course covers a wide range of topics, including data analysis, machine learning, statistical modeling, data visualization, and more. It provides a holistic understanding of the data science lifecycle.
Hands-on Projects
Gain practical experience through real-world projects, enabling you to apply your skills to solve industry-relevant problems.
Industry Experts as Instructors
Learn from experienced data scientists who bring a wealth of industry knowledge and insights into the classroom.
Cutting-edge Tools and Technologies
Get hands-on experience with popular data science tools such as Python, R, TensorFlow, and Tableau, ensuring you are well-versed in the latest technologies used in the field.
Networking Opportunities
Connect with industry professionals and fellow data science enthusiasts through workshops, guest lectures, and networking events.
Scope of Data Scientists in India
Data science has gained significant recognition in the 21st century, being hailed as a highly sought-after profession. It has consistently topped LinkedIn's Emerging Jobs List for several years, highlighting its growing importance in the job market. The COVID-19 pandemic in 2021 further emphasized the need for data analysis skills, with approximately 82,000 global job openings in this field. India, in particular, experienced a notable 45% increase in the adoption of artificial intelligence during this time.
As digital transformation continues to accelerate, the demand for data scientists is on the rise. The field offers lucrative career opportunities, with competitive salaries. In Hyderabad, for example, the average salary for a data scientist is around Rs 6 lakhs, with entry-level positions earning between Rs 5-6 lakhs. For those who continually learn, upgrade their skills, and stay up-to-date with industry trends, the compensation package can exceed Rs 24 lakhs or even crores.
Career Opportunities in Data Science
Data science offers a plethora of rewarding career opportunities. Here are a few prominent roles:
OUR HIRING PARTNERS
WHY CHOOSE AIMNXT?
Industry-Relevant Curriculum
Our curriculum is carefully crafted by industry experts to align with the latest trends and demands of the data science field.
Experienced Faculty
Our instructors are seasoned data scientists who possess deep domain expertise and practical knowledge.
Hands-on Approach
We emphasize practical learning through hands-on projects, enabling you to develop the necessary skills and experience.
Placement Assistance
We provide dedicated support and guidance to help you secure job placements and internships in top organizations.
Learning Environment
Our institute offers a conducive learning environment with state-of-the-art infrastructure, access to relevant resources, and interactive learning methods.
Foundations of Data Science
• Overview of the Data Science Lifecycle
• Role of a Full-Stack Data Scientist
• Tools and Technologies in Full-Stack Data Science
• Linear Algebra, Calculus, and Probability Basics
• Descriptive and Inferential Statistics
• Hypothesis Testing and Confidence Intervals
• Statistical Distributions and Applications
• Python
– Basics of Python Programming
– Advanced Python (OOPs, File Handling, Exception Handling)
– Libraries: NumPy, Pandas, Matplotlib, Seaborn, SciPy, and
Scikit-learn
• R Language
– Basics of R Programming
– Data Manipulation with dplyr and tidyr
– Visualization with ggplot2
– Statistical Modeling in R
Database Management and Big Data
• Database Fundamentals and SQL Basics
• Advanced SQL Queries: Joins, Subqueries, Views, and Indexing
• Data Manipulation and Transaction Management
• Integration with Python and R
• Introduction to Big Data Concepts
• Hadoop Architecture and Ecosystem (HDFS, MapReduce, YARN)
• Working with Hive and Pig for Data Processing
• Basics of Apache Spark for Big Data Analytics
Data Preprocessing and Feature Engineering
• Data Cleaning and Transformation Techniques
• Handling Missing Data, Outliers, and Data Imbalance
• Feature Scaling, Encoding, and Selection
• Dimensionality Reduction Techniques: PCA, t-SNE, LDA
Data Visualization and Business Analytics
• Creating Visualizations with Matplotlib, Seaborn, Plotly, and Dash
• Interactive Dashboards with Power BI
• Tableau Basics: Connecting, Building Dashboards, and Storytelling
• Fundamentals of Business Analytics
• KPI Definition and Business Intelligence
• Case Studies: Sales, Marketing, and Operational Analytics
Machine Learning (ML) and Deep Learning (DL)
• Types of Machine Learning: Supervised, Unsupervised, Reinforcement
• Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forest, SVM, and kNN
• Model Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, and ROC-AUC
• Neural Networks: Architecture and Training
• Deep Learning Frameworks: TensorFlow, PyTorch, and Keras
• Advanced Topics: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
Artificial Intelligence and Azure AI
• Introduction to AI: Applications and Use Cases
• Fundamentals of Knowledge Representation and Search Algorithms
• Introduction to Azure AI Services
• Azure Machine Learning Studio: Building and Deploying Models
• Case Studies: Sales, Marketing, and Operational Analytics
Advanced Topics
• Introduction to Generative AI Concepts
• Applications of GANs and Autoencoders
• Working with OpenAI APIs and Generative Models
• Basics of Prompt Engineering for Generative AI
• Techniques for Crafting Effective Prompts
• Case Studies: Chatbots, Summarization, and Text-to-Image Applications
Cloud Computing and Deployment
• Basics of Cloud Computing and Its Importance in Data Science
• Deploying Machine Learning Models on Azure and AWS
• Working with Cloud Databases and Storage Solutions
• Building APIs using Flask and FastAPI
• Deploying Models with Docker and Kubernetes
• MLOps: Monitoring and Maintaining Deployed Models
Capstone Projects and Case Studies
• Retail Sales Prediction: Using ML and Power BI for actionable insights
• Healthcare Chatbot: Leveraging Generative AI and Prompt Engineering
• Big Data Processing: Using Hadoop and Spark for large-scale analytics
• Real-Time Fraud Detection: Deep learning with TensorFlow
• IoT Data Analysis: Cloud-integrated dashboard with Power BI
Career Development
• Resume Writing and Portfolio Building for Data Science Roles
• Preparing for Technical Interviews and Mock Sessions
• Industry Projects to Strengthen Real-World Skills
Course Outcomes
• 1. Master Python, R, SQL, Hadoop, and Azure AI for data science
• 2. Build and deploy machine learning and deep learning models
• 3. Gain expertise in Generative AI and Prompt Engineering
• 4. Analyze data and create impactful visualizations using Power BI
• 5. Complete hands-on projects to showcase real-world applicationsI
FAQs
A: Basic understanding of mathematics, statistics, and programming is beneficial, but not mandatory. Our course is designed to accommodate learners from various backgrounds.
A: This course opens up career opportunities as a data scientist, data analyst, machine learning engineer, business intelligence analyst, and more.
A: The course duration may vary, but typically ranges from 3 to 6 months, depending on the program.
A: You will work on real-world projects that simulate industry scenarios, allowing you to apply your learning and develop practical skills.
A: Yes, we provide post-course support, including access to alumni networks, job placement assistance, and continued learning resources.
A: Yes, upon successful completion of the course, you will be awarded a certificate that validates your skills and knowledge in data science.
LEADERS OF TOMORROW ARE DEVELOPED BY US
We Build leaders for tomorrow. Here are a few success stories that have been created by AIMNXT