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Data Science Course in Bhubaneswar
Unlock the power of data with our Data Science Training in Bhubaneswar. As the demand for data-driven insights grows, mastering data science is essential for career advancement and business success. Our comprehensive data scientist course is designed to equip you with the skills and knowledge needed to excel in this dynamic field.
Data Analyst Courses In Bhubaneswar
We emphasize practical learning, ensuring that you can apply your knowledge to solve business problems and make data-driven decisions. Our training also includes career development guidance and insights into industry trends to help you stay competitive in the evolving data science landscape.
Conveniently located in Bhubaneswar, our center offers modern facilities and a supportive learning environment. Join us to start your journey in data analytics courses in Bhubaneswar and transform your career with essential data science expertise.
Data Science Course Duration and Details
Course Duration
3 months
Course Fees
₹ 20,000
Includes
Python, AI, M, Live project
Course Days
Weekly 3 Days
Course Overview and Modules
- Overview of Data Science and its applications
- Key concepts and tools used in Data Science
- The data science workflow: from data collection to insights
- Introduction to Python programming for data science
- Essential Python libraries (NumPy, pandas, matplotlib)
- Data manipulation and analysis using Python
- Methods for data collection (web scraping, APIs, databases)
- Data cleaning and preprocessing techniques
- Handling missing data and outliers
- Data transformation and feature engineering
- Understanding and summarizing data distributions
- Data visualization techniques (histograms, scatter plots, box plots)
- Identifying patterns and trends in data
- Using statistical summaries and descriptive statistics
- Introduction to probability and statistics
- Hypothesis testing and confidence intervals
- Correlation and regression analysis
- Statistical significance and p-values
- Overview of machine learning concepts and algorithms
- Supervised vs. unsupervised learning
- Model evaluation and validation techniques
- Introduction to common algorithms (linear regression, decision trees, k-means clustering)
- Regression techniques (simple and multiple regression)
- Classification techniques (logistic regression, decision trees, random forests)
- Model tuning and hyperparameter optimization
- Performance metrics (accuracy, precision, recall, F1 score)
- Clustering techniques (k-means, hierarchical clustering)
- Dimensionality reduction (PCA, t-SNE)
- Association rule learning (Apriori, Eclat)
- Evaluating clustering results
- Ensemble methods (boosting, bagging)
- Support Vector Machines (SVM)
- Neural networks and deep learning basics
- Introduction to frameworks (TensorFlow, Keras, PyTorch)
- Advanced data visualization techniques (heatmaps, interactive plots)
- Using visualization tools (Tableau, Power BI)
- Communicating findings through dashboards and reports
- Best practices for presenting data insights
- Introduction to big data concepts and tools
- Overview of Hadoop and Spark
- Data processing frameworks (MapReduce, Spark SQL)
- Handling large-scale data and distributed computing
- SQL for data retrieval and manipulation
- NoSQL databases (MongoDB, Cassandra)
- Data integration from multiple sources
- Database design and management
- Understanding time series data and its components
- Time series forecasting techniques (ARIMA, Exponential Smoothing)
- Anomaly detection in time series data
- Evaluating forecasting models
- Developing end-to-end data science projects
- Model deployment strategies (APIs, cloud platforms)
- Building reproducible workflows
- Documentation and project management
- Understanding data ethics and privacy concerns
- Handling sensitive and personal data responsibly
- Ensuring compliance with data protection regulations (GDPR, CCPA)
- Addressing biases in data and models
- Case studies of data science in various industries (finance, healthcare, marketing)
- Applying data science techniques to real-world problems
- Building and presenting a capstone project