Data Science Course in Hyderabad

BY
Mindmajix Technologies

Gain hands-on experience of advanced principles of data science, such as data analytics and business analytics, to become a professional data scientist.

Mode

Online

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom
Mode of Delivery Video and Text Based
Frequency of Classes Weekdays, Weekends

Course overview

MindMajix Technologies INC. offers the Data Science Course in Hyderabad online certification that is aimed at anyone who wants to master the sophisticated ideas of data science to become certified data scientists and advance in their jobs. The Data Science Course in Hyderabad online course covers Python, R programming, data manipulation, data analytics, statistics, machine learning, and natural language processing.

Data Science Course in Hyderabad online classes by MindMajix Technologies cover all data science principles, from beginner to advanced, as well as best practices in scientific computing, deep learning, artificial intelligence, Keras API, big data, and excel. Students who want to take this course must enroll in one of two ways: self-paced learning lectures or live online classes guided by an expert trainer on Google Meet or Zoom.

The highlights

  • Certificate of completion
  • Certification oriented curriculum
  • Lifetime self-paced video access
  • Flexible schedule
  • Project use cases
  • 30 hours session
  • 20 hours of labs
  • Quizzes & Mocks
  • Free demo on request
  • One-on-one doubt resolution
  • 100% money-back guarantee 
  • 24/7 lifetime assistance

Program offerings

  • Certificate of completion
  • Certification oriented curriculum
  • Lifetime self-paced video access
  • Flexible schedule
  • Project use cases
  • 30 hours session
  • 20 hours of labs
  • Quizzes & mocks
  • Free demo on request
  • One-on-one doubt resolution

Course and certificate fees

certificate availability

Yes

certificate providing authority

Mindmajix Technologies

What you will learn

Data science knowledge Knowledge of data visualization Tableau knowledge Knowledge of big data Machine learning Knowledge of deep learning Natural language processing Knowledge of mongodb Knowledge of excel Programming skills Knowledge of apache spark Statistical skills Knowledge of python

After completing the Data Science Course in Hyderabad certification course, students will obtain a thorough understanding of machine learning and data science principles. Students are taught about data structuresnatural language processingdata visualization, data manipulation, and logistic regression. Students will learn about deep learning, artificial intelligence, and unsupervised learning, as well as how to apply their knowledge to data science operations. Students will also learn about Hadoop, TensorFlow API, Python, MongoDB, Keras, Tableau, SAS, Spark, and MS Excel to utilize their knowledge for data science operations.

The syllabus

Data Science Basics

  • What is Data Science
  • Significance of Data Science in today’s world.
  • R Programming basics

Python Fundamentals

  • Python Introduction
  • Indentations in Python
  • Python data types and operators
  • Python Functions

Data Structures and Data Manipulation

  • Data Structures Overview
  • Identifying the Data Structures
  • Allocating values to the Data Structures
  • Data Manipulation Significance
  • Dplyr Package and performing different data manipulation operations.

Data visualization

  • Introduction to Data Visualisation
  • Various kinds of graphs, Graphics grammar
  • Ggplot2 package
  • Multivariant analysis by using geom_boxplot
  • Univariant analysis by using the histogram, barplot, multivariate distribution, and density plot.
  • Creating the bar plots for the categorical variables through geop_bar() and including the themes through the theme() layer.

Statistics

  • Statistics Importance
  • Statistics classification, Statistical terminology.
  • Data types, Probability types, measures of speed, and central tendency.
  • Covariance and Correlation, Binary and Normal distribution
  •  Data Sampling, Confidence, and Significance levels.
  • Hypothesis Test and Parametric testing

Introduction to Machine Learning

  • Machine Learning Fundamentals
  • Supervised Learning, Classification in Supervised Learning
  • Linear Regression and mathematical concepts related to linear regression
  • Classification Algorithms, Ensemble Learning techniques

Logistic Regression

  • Logistic Regression Introduction
  • Logistic vs Linear Regression, Poisson Regression
  • Bivariate Logistic Regression, math related to logistic regression
  • Multivariate Logistic Regression, Building Logistic Models
  • False and true positive rate, Real-time applications of Logistic Regression

Random Forest and Decision Trees

  • Classification Techniques. Decision Tree Induction Algorithm
  • Implementation of Random Forest in R
  • Differences between classification tree and regression tree
  • Naive Bayes, SVM
  • Entropy, Gini Index, Information Gain

Unsupervised learning

  • Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
  • Unsupervised learning, Clustering algorithm, K-means clustering algorithm
  • K-means theoretical concepts, k-means process flow, and K-means implementation.
  • Implementing Historical Clustering in R
  • PCA(Principal Component Analysis) Implementation in R

Natural Language Processing

  • Natural language processing and Text mining basics
  • Significance and use-cases of text mining
  • NPL working with text mining, Language Toolkit(NLTK)
  • Text Mining: pre-processing, text-classification and cleaning

Mathematics for Data Science

  • Numpy Basics
  • Numpy Mathematical Functions
  • Probability Basics and Notation
  • Correlation and Regression
  • Joint Probabilities
  • Bayes Theorem
  • Conditional Probability, sum rule, and product rule

Scientific Computing through Scipy

  • Scipy Introduction and characteristics
  • Scipy sub-packages like Integrate, Cluster, Signal, Fftpack, and Bayes Theorem

Python Integration with Spark

  • Pyspark basics
  • Uses and Need of pyspark
  • Pyspark installation
  • Advantages of pyspark over MapReduce
  • Pyspark applications

Deep Learning and Artificial Intelligence

  • Machine Learning effect on Artificial Intelligence
  • Deep Learning Basics, Working of Deep Learning
  • Regression and Classification in the Supervised Learning
  • Association and Clustering in unsupervised learning
  • Basics of Artificial Intelligence and Neural Networks
  • Supervised Learning in Neural Networks, multi-layer network
  • Deep Neural Networks, Convolutional Neural Networks
  • Reinforcement Learning, dnn optimisation algorithms
  • Recurrent Neural Networks, Deep learning graphics processing unit
  • Deep Learning Applications, Time series modeling

Keras and TensorFlow API

  • Tensorflow Basics and Tensorflow open-source libraries
  • Deep Learning Models and Tensor Processing Unit(TPU)
  • Graph Visualisation, keras
  • Keras neural-network 
  • Define and Composing multi-complex output models through Keras
  • Batch normalization, Functional and Sequential composition
  • Implementing Keras with tensorboard, customizing neural network training process
  • Implementing neural networks through TensorFlow API

Restricted Boltzmann Machine and Autoencoders

  • Basics of Autoencoders and rbm
  • Implementing RBM for the deep neural networks
  • Autoencoders features and applications

Big Data Hadoop and Spark

  • Big Data and Hadoop Basics
  • Hadoop Architecture, HDFS
  • MapReduce Framework and Pig
  • Hive and HBase
  • Basics of Scala and Functional Programming
  • Kafka basics, Kafka Architecture, Kafka cluster and Integrating Kafka with Flume
  • Introduction to Spark
  • Spark RDD Operations, writing spark programs.
  • Spark Transformations, Spark streaming introduction
  • Spark streaming Architecture, Spark Streaming Features
  • Structured streaming Architecture, Dstreams, and Spark Graphx

Tableau

  • Data Visualisation Basics
  • Data Visualisation Applications
  • Tableau Installation and Interface
  • Tableau Data Types, Data Preparation
  • Tableau Architecture
  • Getting Started with Tableau
  • Creating sets, Metadata and Data Blending.
  • Arranging visual and data analytics
  • Mapping, Expressions, and Calculations
  • Parameters and Tableau prep
  • Stories, Dashboards, and Filters
  • Graphs, charts
  • Integrating Tableau with Hadoop and R

MongoDB

  • MongoDB and NoSQL Basics
  • MongoDB Installation
  • Significance of NoSQL
  • CRUD Operations
  • Data Modeling and Management
  • Data Indexing and Administration
  • Data Aggregation Schema 
  • MongoDB Security
  • Collaborating with Unstructured Data

SAS

  • SAS Basics
  • SAS Enterprise Guide
  • SAS functions and Operators
  • SAS Data Sets compilation and creation
  • SAS Procedures
  • SAS Graphs
  • SAS Macros
  • PROC SQL
  • Advance SAS

MS Excel

  • Entering Data
  • Logical Functions
  • Conditional Formatting
  • Validation, Excel formulas
  • Data sorting, Data Filtering, Pivot Tables
  • Creating charts, Charting techniques
  • File and Data security in excel
  • VBA macros, VBA IF condition, and VBA loops
  • VBA IF condition, For loop
  • VBA Debugging and Messaging

Instructors

Mr Abhishek
Instructor
Mindmajix Technologies

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