Get Trained for Data Science Course OnlineCourse 

Learning Outcomes

• Master both basic and advanced SQL commands, gaining the ability to create, manipulate, and query databases efficiently.
• Understand and implement data modelling concepts in SQL, improving your ability to design databases that best suit business or application needs.
• Utilize SQL for data analysis, learning to apply various analytical functions to extract valuable insights from data.

Topics Covered

• The module 2 introduces you to SQL, starting with fundamental commands and concepts, then progressing to more complex queries and structures in the Advanced SQL section. It covers how to structure and organize data optimally using Data Modelling with SQL, which includes understanding and implementing concepts like normalization and relationships. Lastly, the module emphasizes how SQL can be an effective tool for Data Analysis, teaching you to use various analytical functions to generate insights from data and execute complex data analysis tasks efficiently. Download the Curriculum to Know the topics.

Learning Outcomes

• Acquire an understanding of data visualization principles and how they can be implemented using Tableau.
• Master the basics of creating visualizations in Tableau and gradually progress to more advanced visualization techniques.
• Learn to build interactive dashboards and use storytelling to make data insights more compelling and understandable.

Topics Covered

• The module 3 is centered around understanding and utilizing Tableau, a powerful data visualization tool. You'll start with the fundamentals of the software, learning how to import and organize data within the platform. From there, you'll gain proficiency in creating various types of charts and graphics to represent your data visually, supporting a more intuitive understanding of your data's patterns and trends. As you advance, you'll delve into more complex visualization techniques, crafting intricate and insightful visuals. The module culminates in learning how to create comprehensive dashboards and practice the art of storytelling with Tableau. This ability to create a narrative with your data helps to present your findings in a compelling and easily understood manner, facilitating data-driven decision-making processes. Download the Curriculum to Know the topics.

Learning Outcomes

• Acquire foundational knowledge of machine learning, its applications, and problem setting.
• Understand the principles of supervised and unsupervised learning.
• Learn about model overfitting, underfitting, validation, and evaluation metrics.

Topics Covered

• The module 4 covers the basics of machine learning, starting with a foundational understanding of the field and problem setting in machine learning. It covers two main types of machine learning: supervised learning, where the model learns from labeled data, and unsupervised learning, where the model identifies patterns in unlabeled data. It also discusses concepts such as overfitting and underfitting, which refer to the model's performance on training and unseen data. The module also covers model validation techniques to assess the accuracy and quality of models, and evaluation metrics that help quantify the performance of models. Download the Curriculum to Know the topics.

Learning Outcomes

• Take a deep dive into tree models and ensemble methods in machine learning.
• Learn about time series forecasting, model selection, and validation.
• Gain skills in feature engineering, neural networks, and basics of deep learning.

Topics Covered

• The module 5 continues the journey into machine learning, providing a deep dive into tree models, a type of predictive modelling technique. It introduces the concept of time series forecasting, which is crucial in many business scenarios. It also covers ensemble methods in machine learning, which combine predictions from multiple models to improve overall performance. The module moves forward with model selection and validation techniques. It also introduces feature engineering techniques, a process of creating new features from existing data to improve model performance. Then, you will get an introduction to neural networks, a type of machine learning model inspired by the human brain, and the basics of deep learning, a subfield of machine learning involving neural networks with multiple layers. The module concludes with data storytelling and interpretation, a key aspect of presenting your data analysis and model results. Download the Curriculum to Know the topics.

Learning Outcomes

• Understand the basics of NLP and text processing.
• Gain practical experience in working with text data in Python.
• Learn about feature extraction from text, sentiment analysis, and text classification.

Topics Covered

• The module 6 starts with an introduction to Natural Language Processing (NLP), a field at the intersection of computer science, artificial intelligence, and linguistics. You will learn the basics of text processing, including techniques to clean, normalize, and prepare text data for analysis or model building. Working with text data in Python is then covered, giving you hands-on experience. The module progresses into feature extraction from text, a crucial step in transforming text data into a format that can be analyzed or fed into models. The module then discusses sentiment analysis, a method used to identify and extract subjective information from text data, and text classification, a common task in NLP where text is categorized into predefined classes. Download the Curriculum to Know the topics.

Learning Outcomes

• Apply the skills and knowledge acquired from the course in a practical work setting.
• Gain experience working on real data science projects.
• Develop professional skills such as teamwork, communication, and problem-solving.
• Receive feedback on performance and areas of improvement.
• Understand the challenges and dynamics of working in a data science role.

Topics Covered

• In the final module, Interns will undertake a one-month virtual internship to apply the skills and knowledge they have acquired. You'll gain practical experience and receive feedback on their work, which is vital in transitioning from classroom learning to real-world application. This hands-on experience will help the course to understand and navigate the challenges of working as a data scientist. Download the Curriculum to Know the topics.

Learning Outcome

• Understand and apply the concepts of data cleaning, preprocessing, and visualization using various libraries and tools.
• Gain proficiency in creating interactive dashboards and utilizing advanced Excel functions for data analysis.
• Learn to use SQL for data analysis and time series analysis with Pandas and Statsmodels.

Topics Covered

• The Module 2 dives into data analysis and visualization techniques. Key skills include data cleaning and preprocessing with Pandas, and visualization with libraries like Matplotlib, Seaborn, Plotly, and Bokeh. You'll also learn to visualize geospatial data with GeoPandas and Folium. In terms of business intelligence, you'll be introduced to Tableau. The module will also touch on advanced excel functions for data analysis, SQL for data analysis, and time series analysis with Pandas and Statsmodels. Additionally, creating interactive dashboards with Dash and using PowerBI for data analysis and visualization will be covered. Download Curriculum to Know the topics clearly.

Learning Outcome

• Understand the principles of hypothesis testing, statistical significance, and multivariate statistical analysis.
• Learn and apply data imputation techniques, experiment design, and Bayesian statistics.
• Gain skills in advanced SQL queries for data analysis, and statistical analysis using R.

Topics Covered

• The Module 3 will introduce advanced statistical analysis techniques including hypothesis testing, multivariate statistical analysis, correlation and covariance, data imputation techniques, Bayesian statistics, and design of experiments. In addition, you'll learn how to compose advanced SQL queries for data analysis and the use of R for statistical analysis. The module will also delve into non-parametric statistical methods, survival analysis, and the handling of multi-dimensional data. Download Curriculum to Know the topics clearly.

Learning Outcome

• Understand and apply Hadoop and Apache Spark for data processing and analysis, mastering the use of HDFS, MapReduce, and Spark's core functionalities.
• Acquire skills in data ingestion with Apache Oozie and management of NoSQL databases, particularly HBase, along with advanced SQL querying for data analysis.
• Utilize Apache Spark for efficient ETL processes and real-time stream processing, integrating with Apache Kafka and Amazon Kinesis for dynamic data analytics solutions.

Topics Covered

• The module 4 focuses on mastering Big Data ecosystems, with a deep dive into Hadoop and Apache Spark for data processing and analysis. Participants will learn data ingestion using Apache Oozie, explore NoSQL databases like HBase, and enhance their SQL skills for complex data queries. The module also covers Apache Spark's capabilities in ETL processes, real-time streaming, and integration with technologies such as Apache Kafka and Amazon Kinesis for advanced data analytics solutions.

Learning Outcome

• Grasp the essentials of Data Warehousing, its evolution with Big Data, and the critical role of SparkSQL for structured data processing.
• Develop expertise in advanced ETL techniques using Spark, enhancing data transformation, loading capabilities, and overall analytics readiness.
• Understand stream processing fundamentals with a focus on structured streaming and employ integration techniques with Apache Kafka and Amazon Kinesis for real-time analytics.

Topics Covered

• In module 5, you'll learn to explore the essentials of Data Warehousing in the Big Data era, including the evolution of warehousing technologies. It emphasizes proficiency in SparkSQL for structured data processing, advanced ETL techniques with Spark, and the principles of stream processing. The module also delves into the integration of Apache Kafka and Amazon Kinesis with Spark Streaming, equipping learners with the skills for real-time analytics in data pipelines.

Learning Outcome

• Understand and apply techniques for handling categorical features, feature scaling, and normalization.
• Learn dimensionality reduction techniques and how to handle missing data.
• Gain skills in text and image feature extraction, automated feature engineering, and feature selection techniques.

• The Module 6 delves into feature engineering, starting with handling categorical features and techniques for feature scaling and normalization. It will also cover binning and feature discretization, handling missing data, and using dimensionality reduction techniques for feature extraction like PCA and t-SNE. The module also presents text feature extraction techniques such as Bag of Words, TF-IDF, word embeddings, and image feature extraction with pre-trained models. Additionally, you'll learn about automated feature engineering with tools like Featuretools, creating interaction features, encoding techniques for categorical variables, and feature selection techniques. Download Curriculum to Know the topics clearly.

Learning Outcome

• Understand and apply various supervised learning algorithms, including linear regression, logistic regression, and tree-based models.
• Gain an understanding of neural networks, bias-variance tradeoff, and gradient boosting algorithms.
• Learn techniques for model interpretation, handling imbalanced datasets, and using ensemble methods.

Topics Covered

• The Module 7 provides a thorough introduction to supervised learning algorithms. This includes understanding and implementing linear regression, logistic regression, and tree-based models like decision trees, random forests, and XGBoost. You'll also explore support vector machines, gain an introduction to neural networks, and understand the bias-variance tradeoff. Additionally, you'll learn the basics of neural networks including perceptron and multi-layer perceptron, gradient boosting algorithms like LightGBM and CatBoost, and model interpretation techniques like LIME and SHAP. The module concludes with handling imbalanced datasets, using ensemble methods, and a brief introduction to reinforcement learning. Download Curriculum to Know the topics clearly.

Learning Outcome

• Understand and apply clustering techniques, dimensionality reduction techniques, and anomaly detection methods.
• Gain skills in association rules mining and recommender systems.
• Develop an understanding of natural language processing, including topic modeling and sentiment analysis.

Topics Covered

• In this module, you'll delve into unsupervised learning algorithms, starting with k-means and hierarchical clustering techniques. You'll then explore advanced clustering methods such as DBSCAN, OPTICS, and Spectral Clustering, and get an introduction to dimensionality reduction techniques. Additionally, you'll learn about anomaly detection techniques, association rules mining with Apriori and Eclat, and recommender systems such as collaborative filtering and content-based filtering. The module concludes with an introduction to natural language processing, including topic modeling and sentiment analysis. Download Curriculum to Know the topics clearly.

Learning Outcome

• Understand and apply concepts related to deep learning frameworks like TensorFlow, PyTorch, and Keras.
• Learn about various network architectures including CNNs, RNNs, LSTMs, and transformers.
• Gain proficiency in generative deep learning, reinforcement learning with deep learning, and natural language processing with deep learning.

Topics Covered

• The Module 9 covers deep learning and neural networks, starting with an introduction to deep learning frameworks like TensorFlow, PyTorch, and Keras. You'll learn about convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs). The module also introduces the concept of transfer learning, transformers, and attention mechanisms. You'll explore introduction generative deep learning through GANs and style transfer, and reinforcement learning with deep learning techniques like Q-Learning and Deep Q-Networks. Download Curriculum to Know the topics clearly.

Learning Outcome

• Understand and apply evaluation metrics for classification and regression tasks.
• Learn and apply cross-validation techniques and hyperparameter tuning techniques.
• Gain an understanding of model selection techniques, mitigating model overfitting and underfitting, and model interpretability.

Topics Covered

• In this module, you'll understand and apply evaluation metrics for classification tasks like confusion matrix, ROC, AUC, and for regression tasks like MSE, RMSE, MAE, and R-Squared. It will also introduce you to cross-validation techniques like K-Fold, Stratified, and Time-Series. Hyperparameter tuning techniques such as grid search, random search, and Bayesian optimization will be covered. Additionally, you'll learn about model selection techniques, regularization, stepwise regression, and understand how to mitigate model overfitting and underfitting. The module wraps up with an introduction to model interpretability and concepts of model fairness, accountability, transparency, and ethics (FATE). Download Curriculum to Know the topics clearly.

Learning Outcome

• Understand and apply concepts related to model deployment, including using Docker and cloud-based AI platforms.
• Gain skills in MLOps principles, including continuous integration and deployment for ML models.
• Learn about monitoring and maintaining models in production, A/B testing, serverless deployment, and scaling ML models.

Topics Covered

• In the final technical module, you will learn about model deployment strategies using Flask, Django, and cloud-based AI platforms. You'll explore the containerization of models using Docker and get an introduction to MLOps (DevOps for machine learning). The module will also cover continuous integration and deployment for ML models with tools like Jenkins, monitoring and maintaining models in production, A/B testing, and multivariate testing. Moreover, you'll learn about serverless deployment using AWS Lambda and Google Cloud Functions, scaling ML models, and using Kubernetes for managing deployed models. The module concludes with a discussion on ethics, privacy, and legal considerations in model deployment. Download Curriculum to Know the topics clearly.

Learning Outcome

• Apply all learned data science concepts in a real-world context, enhancing practical understanding and experience.
• Develop professional skills such as teamwork, project management, and communication in a data science environment.
• Gain insights into industry practices, challenges, and strategies in the field of data science.

Topics Covered

• In the concluding module, you will apply all of the learned data science concepts in a real-world context during a three-month virtual internship program. This will help you develop practical skills, enhance your understanding and experience in data science, and expose you to industry practices, challenges, and strategies. This hands-on experience will prepare you for a successful transition into a professional data science role. Download Curriculum to Know the topics clearly.

Learning Outcome

• Learn the core principles of generative AI, distinguishing between generative and discriminative models.
• Explore GANs, transformers, and diffusion models, understanding their architecture and applications in creating realistic digital content.
• Apply generative AI techniques across various fields like art, music, and synthetic data generation, showcasing its broad impact.

Topics Covered

• In the add-on module, Generative AI offers an immersive dive into the world of generative models, distinguishing between generative and discriminative models. It covers GANs (Generative Adversarial Networks) in detail, from their architecture to their capability in producing lifelike images and videos. Additionally, the module explores the impact of transformers like GPT (Generative Pre-trained Transformer) in generating human-like text and their wider applications. It introduces diffusion models, a novel generative model class, renowned for their high-quality image generation, and discusses the versatile applications of generative AI across art, music, and synthetic data generation for AI training, highlighting its transformative potential across various fields.