Professional Certificate in Big Data & Analytics

Get introduced to the world of Big Data and understand the 4 V’s which define Big Data. Learn about the challenges concerning Big Data and the workaround technique called distributed framework tools used for churning Big Data. Learn how these challenges Big Data is addressed by a distributed computing framework.

Learn about the most user-friendly and the first multi-user operating system which is the preferred OS for the implementation of an open-source distributed framework tool called Hadoop. The filesystem for the Hadoop framework should be distributed to handle the huge amount of data. The filesystem of Linux OS (ext3, ext4, and xfs) are capable of supporting the distributed framework. Having hands-on exposure on Linux OS is a very relevant requirement to excel in working with Big Data tools. You will learn to install and work with Linux OS. You will also learn to install a pseudo-single-node Hadoop environment cluster. Hadoop Distributed File System.

Learn how HDFS stores a huge volume of data without data loss and fault tolerance. You will understand the concepts of replication and partitioning that is used in HDFS. Learn about the java background services also known as Demons working to make Hadoop capable of storing Big Data that cannot be fit into a single System.

Learn the logic of the distributed computing framework implemented by Google. Learn the concept of Map jobs and Reduce jobs. Learn how Mapper functions and Reducer functions work in tandem to process huge volumes of data. Understand the functionality of the processes of the MapReduce component of Hadoop. Understand input splits and learn how they are different from blocks in HDFS.

Understand the Big Data Ecosystem and its projects. Learn about the drawbacks of distributed computing, MapReduce framework. You have learned about the low-level language used for MapReduce framework, Apache Pig is a high-level programming language to assist the developers. Learn about the high-level programming languages developed by Yahoo on the MapReduce framework. Learn about the ETL tool Apache Pig, the features, components and the execution model. Learn about the ways to execute the Apache Pig Latin scripts on Mapreduce and Local mode.

An open-source programming tool developed by Facebook to handle structured data on Big Data framework. Get introduced to the SQL programming tool, Apache Hive. Understand its applications as a Data warehousing tool. You will learn how Hive manages and handles the schema of the tables created using an RDBMS database called Metastore. Learn about internal and external tables that can be created using Hive.

Learn about the first database on the distributed file system & HBase. Understand how NoSQL databases are different from SQL based databases. Learn about the installation of HBase on Hadoop, its use and advantages. Understand the architecture of HBase and its components. Learn about Hfiles and Memstore concept used in HBase to store the data.

Understand how enterprises use tools to move the data from legacy systems on to Big data. Learn about the concept of Data Ingestion. Understand the need to migrate the data from a traditional database system (SQL) to Big Data tools. Learn about quick migration of data into HBase tables from RDBMS systems and vice versa. Learn to use the open-source tool SQOOP (the combination of Hadoop and SQL) to create a pipeline from the SQL database to Hadoop.

Understand the need for a new age tool to handle the Big Data as the latency of MapReduce programs are very high. Learn about the lightning-fast Unified stack programming language framework in the Analytics community which was developed for general purpose, in-memory computing to attain super speeds of execution, and distributed computing - Apache Spark. Understand Apache Sparks architecture and its building blocks and components. You will learn about the default data abstraction used by spark called RDD.

Understand various data sources and why organizations are gearing up to store the data like never before. Learn on what are the various applications of data science in various industries ranging from FSI to LSHC to Retail and many more. Also one will appreciate the job opportunities in the space of data science, data modeling, and data analysis. Finally understand the golden rule on how to become a successful data scientist, data modeler, data analyst, etc.

Learn about the Project Management Methodology, CRISP-DM, for handling Data Science projects and various concepts used in defining business problems and then performing data collection in line with business problems. Understand the importance of documenting the business objectives and business constraints so that the entire project is performed to solve business problems. Project charter overview will help participants understand the real-world documentation aspect as well.

Learn about data preparation and data cleansing in data science projects to ensure that appropriate data is provided to the next step. Outlier analysis or treatment, handling missing values using imputation, transformation, normalization/standardization, etc., will be explained in thorough detail. Understand the various moments of a business decision and graphical representation so that structured descriptive analytics or descriptive statistics is performed. This exploratory data analytics is the first step in data analytics to draw meaningful insights.

Learn about applying domain knowledge to the data so that more meaningful variables are derived. Understand two main modules of feature engineering including feature extraction and feature selection. Knowing how to shortlist the critical inputs from trivial many inputs is the key to ensuring the high performance of the machine learning models. Understand about extracting features from structured as well as unstructured data such as videos, images, audio, textual files, etc.

Understand one of the key inferential statistical techniques called Hypothesis testing. Understand various parametric hypothesis tests. Learn about the implementation of a Regression method based on the business problems to be solved. Understand about Linear Regression as well as Logistic Regression techniques used to handle continuous as well as discrete output prediction. Evaluation techniques by understanding the measure of Error (RMSE), problems while building a Regression Model like Collinearity, Heteroscedasticity, overfitting, and Underfitting are explained in detail.

Understand the advanced regression models such as Poisson Regression, Negative Binomial Regression, Zero-Inflated models, etc., used to predict the count output variables. Learn about the various scenarios which trigger the application of advanced regression techniques. Understanding and evaluating the models using appropriate performance and accuracy measures of regression are explained in detail.

Data Mining branch called unsupervised learning is extremely important in solving problems, which require the application of only unsupervised learning tasks and also used to support predictive modeling. Clustering or segmentation has two prime techniques – K-Means clustering, as well as Hierarchical clustering and both, are explained in finer detail. Alongside, participants will also learn about handling datasets with large variables using dimension reduction techniques such as Principal Component Analysis or PCA. Finally one will learn about Association rules also called affinity analysis or market basket analysis or relationship mining.

The majority of unstructured data is in textual format and analyzing such data requires special techniques such as text mining or also called as text analytics. Techniques such as DTM/TDM using Term Frequency, Inverse Document Frequency, etc. are explained in this module. One will also learn about generating a word cloud, performing sentiment analysis, etc. Also, advanced Natural Language Processing techniques such as LDA, topic mining, etc., are explained using practical use cases. Also, the learning includes extracting unstructured data from social media as well as varied websites.

A major branch of study in data science is Machine Learning also called Data Mining Supervised Learning or Predictive Modelling. One will learn about K Nearest Neighbors (KNN), Decision Tree (Boosting), Random Forest (Bagging), Stacking, Ensemble models and Naïve Bayes. One will learn about the various regularization techniques as well as understand how to evaluate for overfitting (variance) and underfitting (bias). All these are explained using industry relevant use cases and mini-projects.

Black box machine learning algorithms are extremely important in the field of machine learning. While there is no interpretation in the models, accuracy is unmatched in comparison to other shallow machine learning algorithms. Learn about the Perceptron algorithm and Multi-layered Perceptron algorithm or MLP. Understand about Kernel tricks used within Support Vector Machine algorithms. Understand about linearly separable boundaries as well as non-linear boundaries and now to solve these using Deep learning algorithms.

Understand the difference between cross-sectional data versus time series data. Search about the forecasting strategy employed in solving business problems. Understand various forecasting components such as Level, Trend, Seasonality & Noise. Also, learn about various error functions and which one is the best given a business scenario. Finally, build various forecasting models ranging from linear to exponential to additive seasonality to multiplicative seasonality.

You will learn about the various components of programming and how to construct basic Python programs with expressions, statements, variables, blocks, functions, comments, logic, and conditionals, which are vital concepts in computer programming. These modules will teach you the fundamentals of data structures and you will learn how to create algorithms and how to test and debug Python code.