DATA SCIENCE ONILINE COURSES IN HYDERABAD
Data Science is one of the most in-demand and impactful fields in today’s digital economy. It combines statistics, programming, and domain knowledge to turn raw data into actionable insights. Whether you’re a beginner or a professional looking to switch careers, understanding the key components of data science is crucial.
WHAT WILL YOU LEARN IN DATA SCIENCE BASIC TO ADDVANCED TOPICS
DATA SCIENCE ONLINE TRAINING BEGINDERS
- Introduction to Data Science and Python
- Introduction to Data Science and its importance in industry
- Introduction to Python programming language and its basic syntax
- Data types, variables, and operators in Python
- Data structures in Python (lists, tuples, and dictionaries)
- Introduction to Jupyter Notebook and data visualization using Matplotlib library
- Data Manipulation and Analysis
- Data cleaning and preparation using Pandas library
- Data exploration and visualization using Pandas and Seaborn libraries
- Introduction to statistical concepts (mean, median, mode, standard deviation)
- Hypothesis testing and statistical inference
- Introduction to machine learning and its applications in data science
- Machine Learning Algorithms
- Linear Regression
- Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines
- Clustering algorithms (K-means and Hierarchical)
- Advanced Topics in Data Science
- Neural Networks and Deep Learning
- Natural Language Processing (NLP) and Sentiment Analysis
- Time Series Analysis and Forecasting
- Data Ethics and Privacy
- Project presentations and wrap-up
DATA SCIENCE INTERMEDIATE
- Data Exploration and Preparation
- Advanced data cleaning techniques
- Data transformation and feature engineering
- Handling missing data and imputation techniques
- Outlier detection and treatment
- Exploratory data analysis (EDA) and data visualization using advanced libraries (e.g., Plotly, Bokeh)
- Machine Learning Algorithms
- Advanced linear regression (e.g., ridge, lasso, elastic net)
- Tree-based models (e.g., gradient boosting, XGBoost)
- Support vector machines (SVMs) and kernel methods
- Neural networks and deep learning (e.g., convolutional neural networks, recurrent neural networks)
- Model selection and evaluation techniques (e.g., cross-validation, bias-variance trade-off, overfitting and underfitting)
- Advanced Data Science Techniques
- Unsupervised learning techniques (e.g., principal component analysis, clustering
- Natural language processing (NLP) techniques (e.g., topic modeling, sentiment analysis)
- Recommender systems and collaborative filtering techniques
- Time series analysis (e.g., ARIMA, SARIMA)
- Big data processing and distributed computing frameworks (e.g., Hadoop, Spark)
- Real-world Data Science Applications
- Data ethics and privacy considerations in real-world applications
- Case studies in fraud detection and anomaly detection
- Case studies in customer segmentation and churn prediction
- Case studies in natural language processing and text analytics
- Capstone project presentation and wrap-up
- Note that this curriculum assumes prior knowledge of programming, statistics, and machine learning concepts, and is designed for those who have completed an introductory data science course
DATA SCIENCE EXPERT
- Advanced Machine Learning
- Deep reinforcement learning
- Convolutional neural networks (CNNs) for computer vision
- Recurrent neural networks (RNNs) for natural language processing (NLP)
- Generative adversarial networks (GANs) for data synthesis
- Transfer learning and domain adaptation
- Bayesian Statistics and Probabilistic Programming
- Introduction to Bayesian statistics
- Bayesian inference and Markov Chain Monte Carlo (MCMC) methods
- Probabilistic programming languages (e.g., PyMC3, Stan)
- Hierarchical modeling and model comparison
- Bayesian optimization and decision making
- Advanced Data Engineering and Big Data Processing
- Distributed computing frameworks (e.g., Spark, Hadoop)
- Streaming data processing and real-time analytics
- Data engineering for machine learning pipelines
- NoSQL databases and graph databases
- Data governance, security, and compliance
- Advanced Topics in Data Science
- Causal inference and counterfactual analysis
- Explainable AI and interpretable machine learning
- Automated machine learning (AutoML) and meta-learning
- Ethical considerations in AI and data science
- Capstone project presentation and wrap-up
COMPONENTS OF DATA SCIENCE
- DATA COLLECETIONS
- MACHINE LEARNING WITH AI
- EXPLORATORY DATA ANALYSIS
- BIG DATA AND CLOUD COMPUTING
- DATA CLEANING AND PREPROCESSING
- DEEP LEARNING(DL)
- DATA VISUALIZATION
Career opportunities
Machine Learning Engineer:Designs, develops, and deploys machine learning models to solve complex problems. Works with large datasets, trains and fine-tunes algorithms, and integrates AI solutions into production systems. Collaborates with data scientists and engineers to ensure scalable, efficient, and accurate models. Continuously improves performance through experimentation, evaluation, and optimization of data pipelines and model architectures
Data Engineer:Designs, builds, and maintains scalable data pipelines and architectures for efficient data processing. Works with structured and unstructured data, ensuring data quality, integrity, and accessibility. Collaborates with data scientists, analysts, and engineers to support analytics and machine learning workflows. Optimizes data storage and retrieval, enabling real-time and batch data processing across systems.
Data Scientist:Analyzes complex data to extract actionable insights and drive data-informed decisions. Builds predictive models, performs statistical analysis, and applies machine learning techniques to solve real-world problems. Works closely with cross-functional teams to understand business needs and deliver impactful data solutions. Communicates findings through visualizations and reports to support strategic initiatives
AI Specialist:Develops and implements artificial intelligence solutions to automate processes and enhance decision-making. Applies machine learning, deep learning, and natural language processing to build intelligent systems. Collaborates with engineers and stakeholders to integrate AI into products and services. Continuously researches and experiments with emerging AI technologies to drive innovation and improve model performance.
Research Scientist:Conducts advanced research to develop new theories, models, and technologies in a specialized field. Designs and executes experiments, analyzes data, and publishes findings in scientific journals. Collaborates with academic, industry, or cross-functional teams to push the boundaries of knowledge and innovation. Applies rigorous scientific methods to solve complex problems and contribute to long-term strategic goals.
Data Scientist:Leverages statistical analysis, machine learning, and data visualization to uncover insights from complex datasets. Develops predictive models and data-driven solutions to support strategic decision-making. Collaborates with cross-functional teams to define problems, gather requirements, and deliver actionable outcomes. Communicates findings clearly to both technical and non-technical stakeholders.
Uses of learning Data Science Course online
Secure future
As data science is a growing field it requires more professionals to work in it making you get a good career
Foundation for AI& machine learning
Establish a strong foundation in AI and machine learning to drive innovation and future success
Work- opportunities
Explore diverse work opportunities to unlock growth, development, and career advancement
Certification
Earn certifications to validate skills, enhance expertise, and boost career opportunities in your field.
High-income salaries
Data science jobs offer good salaries even at entry-level positions.
High demand and job opportunities
Fields with high demand offer abundant job opportunities, ensuring career growth and stability.