Best Data Science Course Online 

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 from the fundamental commands and concepts moving into Advanced SQL section on the way, as far as handling complex queries and structures go, covers structuring and organizing the data most appropriately by making you familiar with ideas such as data modeling and application of SQL about normalization and relations. Lastly, this module teaches and emphasizes how SQL can be quite an effective tool for Data Analysis, teaching ways to use the different analytical functions to generate insightful results from your data and completing 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 focuses on getting to know and using Tableau, a highly effective data visualization tool. It will begin by introducing you to the basics of the software. You will be learning how to import and arrange data within the program. Next, you'll master the different types of charts and graphics to visually represent your data, further supporting an intuitive grasp of the patterns and trends of your data. As you progress, you'll explore more advanced visualization techniques, developing complex and informative visuals. The module concludes by learning how to create comprehensive dashboards and the art of storytelling with Tableau. This will help you tell a story with your data, enabling you to communicate your findings in an engaging and accessible way that makes data-informed decision-making easier. 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 introduces the module on the fundamentals of machine learning. First is the introduction into the basic understanding of the subject and problem-setting in machine learning. Two basic types of machine learning are prevalent: one for supervised learning that learns from a labeled set and the other with unsupervised learning that has to find hidden patterns within unlabelled data. It also presents ideas such as overfitting and underfitting, which describe the model's performance in terms of the fitting or association towards the training as well as unseen data. It also includes validation techniques for judging the accuracy and quality of the models and some evaluation metrics with the aim of quantifying the performance of the 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 develops the machine learning topic, explaining predictive modeling techniques using tree models. A key part is introduced about time series forecasting. This is a very important topic in many applications of business. The other main extension of machine learning is known as ensemble methods, where predictions from multiple models are combined to achieve a better level of performance.  This includes model selection and validation, introducing techniques of feature engineering-the ways by which new features may be produced from the given data to better performance in modeling. Then, the neural networks-in other words, what is a learning model inspired from the way human brains work-come into focus, along with the basics of deep learning-the subfield of machine learning applying neural networks, more than just one layer-are introduced, and finally, data storytelling and interpretation-end-to ensure an effective communication of your data analysis and model outcomes. Download 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 will introduce you to Natural Language Processing, an area that sits at the crossroads of computer science, artificial intelligence, and linguistics. It introduces basic text processing, such as cleaning and normalization, to prepare text data for analysis or model building. Hands-on experience with text data in Python follows. The module continues to feature extraction from text, an important step of extracting text data into analyzable, or feed-to-model format. Then the module discusses sentiment analysis wherein the primary purpose is to find and elicit subjective text information, and then there is text classification, which is quite a common task in the case of NLP that concerns the categorization of text information into predefined categories.  Download the Curriculum 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 delves into data analysis and visualization techniques. The skills included are data cleaning and preprocessing using Pandas and visualization with libraries such as Matplotlib, Seaborn, Plotly, and Bokeh. You will learn to visualize geospatial data using GeoPandas and Folium. In business intelligence, you are introduced to Tableau. It will also cover advanced excel functions for data analysis, SQL for data analysis, and time series analysis with Pandas and Statsmodels. In addition, it will include creating interactive dashboards with Dash and using PowerBI for data analysis and visualization. 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 also introduce advanced techniques in statistical analysis such as hypothesis testing, multivariate statistics, correlation and covariance, imputation techniques in data, Bayesian statistics, design of experiments and more. There is also SQL query composition with a focus on data analysis along with the usage of R to analyze statistics. Non-parametric methods of statistical analysis, survival analysis, handling multi-dimensional data are also taken into consideration for this module. 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 deeper insightful handling and investigation into Hadoop and Apache Spark, and data processing and analysis. Participants will learn how to ingest data using Apache Oozie. Participants will also explore NoSQL databases like HBase, enhance their SQL skills in complex queries.

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 the fundamentals of Data Warehousing in the age of Big Data, from where warehousing technology has come of age. Emphasis is put on SparkSQL for processing structured data, high-end ETL techniques using Spark, and basic principles of stream processing. Furthermore, it dives into integrating the use of Apache Kafka and Amazon Kinesis along with Spark Streaming to be enabled for real-time analytics in the data pipeline.

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 covers feature engineering, first by handling categorical features and then techniques for scaling and normalization. It will be covering binning and feature discretization, how to handle missing data, and the use of dimensionality reduction techniques for extracting features like PCA and t-SNE. Finally, it has text feature extraction techniques such as Bag of Words, TF-IDF, word embeddings, and image feature extraction with pre-trained models. Moreover, you'll be learning automated feature engineering using tools like Featuretools, how to create interaction features, encoding techniques for categorical variables, and techniques of feature selection. 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 gives an excellent overview of the supervised learning algorithms. That involves understanding and implementation of linear regression, logistic regression, and tree-based models, which are decision trees, random forests, and XGBoost. In addition to these, support vector machines are discussed, with a general introduction to neural networks, as well as understanding bias-variance tradeoff. You'll be introduced to the basics of neural networks which include perceptron and multi-layer perceptron, to gradient boosting algorithms such as LightGBM and CatBoost, and model interpretation techniques including LIME and SHAP. This module ends with imbalanced dataset handling, ensemble methods, and a little outline on 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. Then you will learn about advanced clustering methods like DBSCAN, OPTICS, and Spectral Clustering. Dimensionality reduction techniques will also be introduced to you. Further, you will 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 ends up with an overview of natural language processing, starting from topic modeling and sentiment analysis.  Download Curriculum to clearly know the topics.

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 includes deep learning and neural networks, which starts by introducing the student to deep learning frameworks such as TensorFlow, PyTorch, and Keras. In this module, you will be taught about CNNs, RNNs, and LSTMs.The module introduces transfer learning, transformers, and attention mechanisms in the module. You will get an introduction to generative deep learning through GANs and style transfer. Finally, it discusses reinforcement learning using 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 will learn evaluation metrics for the classification task that includes confusion matrix, ROC, AUC and regression tasks include MSE, RMSE, MAE, R-Squared in this module, besides introducing the techniques of cross-validation like K-Fold, Stratified and Time-Series techniques. Hyperparameter tuning techniques involve grid search, random search and Bayesian optimization techniques.  Additionally, you'll learn about model selection techniques, regularization, and stepwise regression. You'll get to understand how you can avoid both overfitting and underfitting of your model. The module concludes with an introduction to model interpretability and FATE concepts: fairness, accountability, transparency, and ethics. 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 i.e., Model Deployment Strategies with Flask, Django, and Cloud-Based AI Platforms - You will be learning about deploying models in this final technical module. You'll explore the containerization of models using Docker, and you will get an introduction to MLOps, DevOps for machine learning. In the module, you will be covered with continuous integration and deployment of ML models, using tools such as Jenkins. The module also covers continuous integration and deployment of ML models using tools like Jenkins, monitoring and maintaining models in production, A/B testing, and multivariate testing. Serverless deployment will be covered through AWS Lambda and Google Cloud Functions, scaling of ML models, and using Kubernetes to manage deployed models. The module ends with discussing 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 allows one to immerse in the generative model and distinguish between the generative and discriminative models. GANs are covered in depth, including the architecture, its capability of generating lifelike images and videos, and other aspects. In the module, there is further study on how transformers like GPT can create human-like text and their larger applications. It introduces diffusion models, a novel class of generative models that are famous for generating high-quality images and talks about the multiple applications of generative AI in art, music, and the generation of synthetic data for AI training, hence holding great promise in changing various domains.