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Mindmajix offers a world-class data science course in Hyderabad for professionals who want to start a successful career in data science. The trainees will get exposure to different aspects of data science like Python, R programming, data manipulation, data analysis, statistics, machine learning, and natural language processing(NLP). Our data science course in Hyderabad also provides an in-depth understanding of scientific computing, deep learning, artificial intelligence, Keras API, big data, and excel. It also makes you apply your data science skills in real-world applications like speech recognition, internet search, and fraud and risk identification. Join our data science course in Hyderabad to advance your career in a rapidly growing field.
In this module, you will learn the basics of data science and R programming, Importance of Data Science.
Topics covered in this section are:
Learning Outcomes: By the end of this module, you will get a fundamental idea about data science and R programming.
This python fundamentals module discusses the python concepts required for a data scientist.
Topics covered in this section are:
Learning Outcomes: By the end of this module, you will get the basic Python programming knowledge.
This module deals with the basic concepts of data structures and data visualization.
Topics covered in this section are:
Learning Outcomes: Upon completing this module, you will be able to understand the significance of Data structures and Data manipulation in Data science.
This module discusses topics like Data visualization, types of graphs, Ggplot2 package, bar plots creation, Univariant, and Multivariant analysis.
Topics covered in this section are:
Learning Outcomes: At the end of this module, you will be able to visualize the data through different graphs, Ggplot2 package. Also, you will get a real-time experience of bar plot creation, Univariant, and Multivariant analysis.
This Data Science online classroom training module deals with statistics concepts like Classification, Probability Types, Covariance, and Correlation. Along with this, you will learn how to analyze the given data set through Data Sampling, Hypothesis Test, and Binary Distribution.
Topics covered in this section are:
Learning Outcomes: By the end of this module, you will gain practical knowledge of different statistical concepts like Probability types, Hypothesis test, Covariance. You will also be able to work with other statistics techniques like Correlation, Data sampling, Normal and Binary Distribution.
This Machine learning module discusses machine learning basics like Supervise learning, classification, linear regression, and ensemble learning techniques.
Topics covered in this section are:
Learning Outcomes: Upon completing this module, you will get a basic knowledge of machine learning, and you will be proficient in Supervised learning, Linear regression, and Ensemble learning.
In this module, you will learn concepts like logistic regression basics, Bivariate and Multivariate Logistic regression, Poisson Regression. Also, it discusses developing logistic models and logistic regression applications.
Topics covered in this section are:
Learning Outcomes: At the end of this module, you will get practical knowledge of Logistic regression, Linear Regression, Poisson Regression, and Logistic models.
This module discusses topics like classification techniques, implementing random forest, Naive Bayes, Entropy, Information Gain, and Gini Index.
Topics covered in this section are:
Learning Outcomes: Upon completing this module, you will acquire an in-depth understanding of decision tree induction algorithms, implementing the random forest in the R programming.
This module provides a detailed overview of different clustering types, K-means clustering algorithm, K-means clustering concepts, and implementing historical clustering and PCA in R programming.
Topics covered in this section are:
Learning Outcomes: Upon completing this module, you will get a real-time experience of k-means clustering, clustering algorithm, and Principal Component Analysis.
This data science online training module will help you master natural language processing, text mining, and NPL working with text mining.
Topics covered in this section are:
Learning Outcomes: At the end of this module, you will get a working knowledge of Natural Language Processing and Text Mining.
This module discusses mathematical concepts like Probability basics, Bayes theorem, Numpy Mathematical functions, Conditional probability, and Joint probabilities.
Topics covered in this section are:
Learning Outcomes: By the end of this module, you will be able to use probability concepts, Numpy functions, Bayes theorem, Correlation, and Regression in Data Science.
In this module, you will learn how to perform scientific computing through the Scipy library.
Topics covered in this section are:
Scipy Introduction and characteristics
Scipy sub-packages like Integrate, Cluster, Signal, Fftpack, and Bayes Theorem
Learning Outcomes: By the end of this module, you will get a real-time scientific computing experience.
In this module, you will learn the basics, importance, installation, advantages, and applications of Pyspark.
Topics covered in this section are:
Learning Outcomes: At the end of this module, you will acquire practical knowledge of Pyspark.
In this module, you will learn the concepts like Deep learning basics, supervised learning, neural networks basics, deep neural networks, convolutional neural networks, recurrent neural networks, and Deep Learning Graphical Processing Unit(GPU).
Topics covered in this section are:
Learning Outcomes: By the end of this module, you will be able to master the deep learning and artificial intelligence concepts required for a data scientist.
This module teaches you how to use TensorFlow and Keras APIs to develop and deploy machine learning and deep learning models.
Topics covered in this section are:
Learning Outcomes: Upon completing this module, you will be able to build deep learning models and visualize the data through Keras and TensorFlow API.
In this module, you will learn how to use restricted Boltzmann machines and autoencoders in deep learning.
Topics covered in this section are:
Learning Outcomes: At the end of this module, you will achieve hands-on knowledge of Restricted Boltzmann machines and Autoencoders.
This module allows you to master the concepts of Hadoop, MapReduce, Hive, Kafka, Scala, Spark, Kafka, Spark Streaming, and Dstreams.
Topics covered in this section are:
Learning Outcomes: By the end of this module, you will acquire real-time experience of working with HDFS, MapReduce framework, HBase, and Kafka. You will also achieve extensive knowledge of developing Spark programs and performing Spark transformations and Spark RDD operations.
This Tableau module deals with Data Visualisation concepts, Tableau Installation, Tableau Architecture, sets creation, Tableau Dashboards, Stories, Graphs, and Charts. Along with this, you will also learn expressions, data blending, and tableau prep.
Topics covered in this section are:
Learning Outcomes: By the end of this module, you will get a real-time experience of Creating sets, graphs, charts, dashboards for analyzing data. You will also acquire hands-on knowledge of tableau architecture, tableau installation, tableau prep, and integrating Tableau with R and Hadoop.
This MongoDB module will help you master the concepts like MongoDB basics, MongoDB installation, CRUD operations, Data Indexing, Data Modeling, and Data Administration. Along with this, you will also learn Data Aggregation Schema and Security concepts.
Topics covered in this section are:
Learning Outcomes: At the end of this module, you will get hands-on knowledge of using MongoDB for performing different database operations like creating a database, inserting data into a database, deleting and updating the data. You will also be able to master data modeling, data Indexing, and data administration.
This module deals with the SAS analytic concepts like functions, operators, data sets creation, procedures, graphs, and macros. You will also learn some advanced concepts of SAS.
Topics covered in this section are:
Learning Outcomes: By the end of this module, you will be able to carry out advanced data analysis by using SAS concepts.
This data science online classroom training module deals with excel concepts like conditional formatting, data filtering, pivot tables, logical functions, and creating charts. Along with this, you will also learn how to use VBA concepts for data analysis.
Topics covered in this section are:
Learning Outcomes: At the end of this module, you will acquire a working knowledge of excel and VBA.
Most of the Data Science Jobs in the industry expect the following add-on skills. Hence, we offer these skills-set as FREE Courses (Basics) to ease your learning process and help you stay ahead of the competition.
Our Data Science Course course aims to deliver quality training that covers solid fundamental knowledge on core concepts with a practical approach. Such exposure to the current industry use-cases and scenarios will help learners scale up their skills and perform real-time projects with the best practices.
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30 hrs of Self-Paced Videos
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30 hrs of Remote Classes in Zoom/Google meet
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Mar 29 - Apr 13
07:00 PM
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Apr 01 - Apr 16
07:00 PM
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Apr 05 - Apr 20
09:00 AM
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Apr 08 - Apr 23
09:00 AM
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Abhishek , having 7+ yrs of experience
Specialized in:Data Science, AI & Machine Learning, Python
Passion towards teaching made Abhishek share the industrial experience he has got for further generations. He has got a total of three years into the real-time industrial background and has trained over 520+ students.
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I have learned a lot from this training which I missed in my previous training institute. You helped me understand the data science concepts really well. Thank you MindMajix for your efforts.
Stamford, Connecticut, USA
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