•  Understand the fundamentals of statistics
• Learn how to work with different types of data
• How to plot different types of data
• Calculate the measures of central tendency, asymmetry, and variability
• Calculate correlation and covariance
• Distinguish and work with different types of distributions
• Estimate confidence intervals
• Perform hypothesis testing
• Make data-driven decisions
• Understand the mechanics of regression analysis
• Carry out regression analysis
• Use and understand dummy variables

Understand the concepts needed for data science even with Python and R! – Hands-On with R & Python

• Data Preprocessing
• Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
• Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
• Clustering: K-Means, Hierarchical Clustering
• Association Rule Learning: Apriori, Eclat
• Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
• Natural Language Processing: Bag-of-words Model and algorithms for NLP
• Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
• Dimensionality Reduction: PCA, LDA, Kernel PCA
• Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Learn and master the most popular big data technologies – all sorts of distributed systems data scientists may need to integrate with

• Install and work with a real Hadoop installation right on your desktop with Hortonworks (now part of Cloudera) and the Ambari UI
• Manage big data on a cluster with HDFS and MapReduce
• Write programs to analyze data on Hadoop with Pig and Spark
• Store and query your data with Sqoop, Hive, MySQL, HBase, Cassandra, MongoDB, Drill, Phoenix, and Presto
• Design real-world systems using the Hadoop ecosystem
• Learn how your cluster is managed with YARN, Mesos, Zookeeper, Oozie, Zeppelin, and Hue
• Handle streaming data in real-time with Kafka, Flume, Spark Streaming, Flink, and Storm
• Apply momentum to backpropagation to train neural networks
• Apply adaptive learning rates procedures like AdaGrad, RMSprop, and Adam to backpropagation to train neural networks
• Understand the basic building blocks of Theano
• Build a neural network in Theano
• Understand the basic building blocks of TensorFlow
• Build a neural network in TensorFlow
• Build a neural network that performs well on the MNIST dataset
• Understand the difference between full gradient descent, batch gradient descent, and stochastic gradient descent
• Understand and implement dropout regularization in Theano and TensorFlow
• Neural network using Keras, PyTorch, CNTK & MXNet

• Use Jenkins to perform Continuous Integration within your Software Development Lifecycle
• Install Jenkins using docker
• Configure Jenkins “The DevOps way”, using Docker, Jobs DSL and Jenkins Pipelines
• Use plugins to integrate Jenkins with popular development software
• Configure the authentication and authorization options to tighten security on your Jenkins UI