Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.

  • Anyone who wants to learn Data Science
  • Data science is a growing field. A career as a data scientist is ranked at the third-best job in America for 2020 by Glassdoor and was ranked the number one best job from 2016-2019

In order to become a data scientist, there is a significant amount of education and experience required. The first step in becoming a data scientist is to earn a bachelor’s degree, typically in a quantitative field. Coding boot camps are also available and can be used as an alternate pre-qualification to supplement a bachelor’s degree. Most data scientists also complete a master’s degree or a Ph.D. in a quantitative/scientific field. Once these qualifications are met, the next step to becoming a data scientist is to apply for an entry-level job in the field. Some data scientists may later choose to specialize in a sub-field of data science.

As on average 14986 data, science-related job openings are available on Naukri every day.

Data Science Training Course Content

  • 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 scientist 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 rate 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
  • Understand and implement batch normalization in Theano and Tensorflow
  • Neural network using Keras, PyTorch, CNTK & MXNet
  • DevOps : CI/CD with Jenkins using Pipelines and Docker
  • 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
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Best data science Training Institute in Hyderabad Madhapur

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