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Data Science Training Fort Worth - Using R & Python

  • Make your career in Data Science with our particular and Certification-driven Data Science Training in Fort Worth.
  • Become a data concentric association/industry's driving asset by expanding express data science and AI aptitudes.
  • Learn to oversee powerful web advancements like Python, R, Spark and Hadoop, for extracting information and bits of knowledge from data.

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Why Data Science Certification Training in Fort Worth?

Here are a few convincing reasons why certification in Data science via Data Science classes Fort Worth is soaring high.

1,90,000 Jobs

The US incorporating Fort Worth leads in the Data Science market and will make 1,90,000 Data Scientists employments.

Top 50 Jobs

Data Scientist work is positioned as one of the main 50 occupations in the US including Fort Worth.

Salesforce Growth

You Should Join Our Classes If You Are:

  • Just starting off & aren’t sure where to start from
  • In an established role but need to dive deep
  • Looking to brush up your skills & master the course
  • Willing to get better in your current or new job

11% Growth

The Bureau of Labor Statistics predicts the Data Science field will grow by 11% by 2024.

High Entry-level Pay

As per Glassdoor, Data Science is one of the highest paying entry-level jobs in the US including Fort Worth.

Why Making a Career in Data Science is a Smart Move?

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Get all the technical skills to help businesses transform their big data!

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IT

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Recruitment

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Medicine/Healthcare

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Analytics

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Bank/Finance

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Semiconductors

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Manufacturing

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Energy/utility

Data Scientist

Data Analyst

Data Engineer

Business Intelligence - BI Developer

Some Hilarious & Concealed Facts About Fort Worth

  • The city has every activity/job located at close vicinity - It will be quite easy for you to walk to your Data Scientist/Analyst’s job real quick.
  • Fort Worth has high-paying salary jobs - 30% Fort Worthers have 6 figures salary - it’s like you too can achieve that by seeding demanded Data Science skills.
  • With more than 25,000 residents, downtown Fort Worth is an active and vibrant setting
  • It is the 13th-largest city in the United States and 5th-largest city in Texas providing huge opportunities for Business.
Fort Worth is in Tarrant County and is one of the best places to live in Texas. Living in Fort Worth offers residents a dense suburban feel and most residents own their homes. In Fort Worth there are a lot of parks. Many families and young professionals live in Fort Worth and residents tend to lean liberal.The Dallas-Fort Worth Metroplex was named the 9th best place to retire in the United States. Fort Worth performs well in culture and economics. The cowboy lifestyle is prevalent in Fort Worth, thanks to a rich historic tradition.
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Instructor-led Live Online Data Science Classes


Starting
Duration
Price

calendar-icon129 Mar

WEEKDAY - Filling Fast

8 weeks

09.00 - 10.30 PM EST

USD 1499

USD 1274

Flat 15% Off

calendar-icon112 Apr

WEEKDAY

8 weeks

09.00 - 10.30 PM EST

USD 1499

calendar-icon105 Apr

WEEKEND

6 weeks

08.00 - 11.00 AM EST

USD 1499

calendar-icon126 Apr

WEEKDAY

8 weeks

09.00 - 10.30 PM EST

USD 1499

calendar-icon110 May

WEEKDAY

8 weeks

09.00 - 10.30 PM EST

USD 1499

USD 1199

Flat 20% Off

Early Bird Discount

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Easy Installments

Detail
WEEKDAY - Filling Fast

calendar-icon129 Mar


8 weeks

09.00 - 10.30 PM EST

USD 1499

USD 1274

Flat 15% Off

Enroll Now
WEEKDAY

calendar-icon112 Apr


8 weeks

09.00 - 10.30 PM EST

USD 1499

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Easy Installments

Not Sure Which Data Science Training Fort Worth Class to Join?  

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Data science Training Course Roadmap

Scroll through the concepts that we cover in our Data Science Course

Data Science With Python OR R Programming

    • What is Data Science
    • Understanding the Data
    • Importance of Data Science
    • How is data science different from BI and Reporting?
    • End to End Data Science Project Life Cycle
    • How does Predictive Analysis Work
    • Primer to R programming
    • What is R? Similarities to OOP and SQL
    • Installation of R and R-studio
    • Types of objects in R – lists, matrices, arrays, data.
    • frames etc.
    • Creating new variables or updating existing variables
    • If statements and conditional loops - For, while etc.
    • String manipulations
    • Subsetting data from matrices and data.frames
    • Casting and melting data to long and wide format
    • Hands-on Assignment, Real Scenarios, MCQs, Practice
    • Tests on IR

Python

    • What is Python?Importance of Python in Data Science
    • Python Installation Guidelines
    • Numerical parameters to represent data
    • NumPy for mathematical computing
    • Scientific computing with Python
    • Text, color map, markers, widths with Matplotlib.
    • Importing and exporting datasets in Python
    • Feature Engineering: Feature Selection and Extraction
    • Practice Tests on Python

Statistical Analysis

    • Statistical parameters to represent data
    • Apply function for parallel processing with Python
    • Statistical and Non-statistical Analysis
    • Population and Sample
    • Statistical Analysis Process,
    • Data Distribution
    • Hands-on Assignment, Real Scenarios, MCQs,
    • Practice Tests on , Statistics
    • Gradient Descent in Boosting Algorithms
    • Multiple linear regression
    • Linear regression vs Logistic regression
    • Understanding Naïve Bayes, Bayes theorem
    • Clustering - K-Means & Hierarchical
    • Hands-on Assignment, Real Scenarios, MCQs,
    • Practice Tests on Machine Learning

Machine Learning

    • Types of Machine Learning
    • What is Supervised, Unsupervised and Reinforcement
    • Decision trees and Random Forest
    • Advantages of tree-based models?
    • Algorithms used in Machine Learning techniques
    • Difference between Data Science, Machine Learning
    • and AI
    • Gradient Descent in Boosting Algorithms
    • Multiple linear regression
    • Linear regression vs Logistic regression
    • Understanding Naïve Bayes, Bayes theorem
    • Clustering - K-Means & Hierarchical
    • Hands-on Assignment, Real Scenarios, MCQs,
    • Practice Tests on Machine Learning

AI,Deep Learning, Natural Language Processing

    • Working with Neural Network
    • Functioning & Usage
    • Convolutional Networks
    • Recurrent Neural Networks
    • AutoEncoders
    • Long Short Term Memory
    • Deep learning with Keras
    • What is TensorFlow?
    • Deep learning with TensorFlow
    • Gradient Descent in Neural Networks
    • Defining and composing models, and deploying
    • TensorBoard.
    • Important Parameters of Perceptron
    • Recursive Neural Tensor Network theory
    • Hands-on Assignment, Real Scenarios, MCQs, Practice
    • Tests on Deep Learning & NLP

Data Science training Certification Course Roadmap

  • What all do we cover in our Data Science Courses

    The Data Science learning path that you get to cover at JanBask Training is very informative and engaging. It has been prepared after vigorous market research on the trends of Data Science, industry needs, etc. Take a look at the things that we cover in this course.

    • What is Data Science?
    • Data Science Life Cycle
    • What is Machine Learning?
    • What is Business Analytics?
    • What is Artificial Intelligence?
    • How is data science different from BI and Reporting
    • End to End Data Science Project Life Cycle
    • knowledge of the concepts of data collection & data mining.
    • Why R and importance of R in Analytics
    • Installation of R and R-studio
    • Working Directories
    • Data Types, Operators, Loops-For and While
    • If-else statements, Nested statements
    • Working with Vector and Matrices
    • Reading and Writing Data in R
    • Working with Data, Manipulating Data
    • Objects, and Vectors
    • Why Python for data science?
    • Overview of Python- Starting with Python
    • Installation of Python
    • Python Editors & IDE’s
    • Understand Jupyter notebook & Customize Settings
    • Concept of Packages/Libraries
    • Installing & loading Packages & Name Spaces
    • Data Types & Data objects/structures
    • List and Dictionary Comprehensions
    • Control flow & conditional statements
    • Definition and computation of the probability
    • Measurement of central tendencies and its applications
    • Spreads, Distributions(Normal, Z-distribution, Binomial, Poisson)
    • Various types of probability distributions(Continuous and discrete)
    • Measures of Central Tendencies and Variance
    • Sampling and Sampling distributions
    • Measures of shape( Skewness and Kurtosis)
    • Measures of the relationship between variables(Correlation, causation)
    • Hypothesis Testing(t-test, Chi-square, Anova)
    • Measures of Dispersion( Variance, std. deviation, Range)
    • Prediction and Confidence interval-Computation and Analysis
    • Correlation, Covariance, and Causation
    • Supervised Learning
    • Algorithms in Supervised learning
    • Regression & Classification
    • Regression vs classification
    • Computation of correlation coefficient and Analysis
    • Multivariate Linear Regression Theory
    • Coefficient of determination (R2) and Adjusted R2
    • Model Misspecifications
    • Economic meaning of a Regression Model
    • Bivariate Analysis
    • Naive Baye classifier, Model Training
    • ANOVA (Analysis of Variance)
    • What is Clustering
    • Supervised vs Unsupervised learning
    • Data Mining Process
    • Measure of distance
    • Hierarchical Clustering / Agglomerative Clustering
    • Non-clustering, K-Means Clustering
    • dimension reduction
    • Advantages of PCA
    • Calculation of PCA weights
    • Definition of a network (the LinkedIn analogy)
    • The measure of Node strength in a Network
    • What is Market Basket / Affinity Analysis
    • The measure of distance/similarity between users
    • Pre-processing, corpus Document-Term Matrix (DTM) and TDM
    • Why is Deep Learning taking off?
    • Advantage of Deep Learning
    • What is the difference between ML, DL and AI?
    • Why is deep learning important?
    • Sharing Variables
    • Activation Functions
    • Illustrate Perceptron
    • Training a Perceptron
    • Neural Networks with TensorFlow
    • Convolutional Neural Networks (CNN)
    • Convolution and Pooling layers in a CNN
    • Understanding and Visualizing a CNN
    • Recurrent Neural Networks (RNN)
    • What is Natural Language Processing?
    • What Can Developers Use NLP Algorithms For?
    • Open Source NLP Libraries
    • Topic Modeling
    • Sentiment Extraction
    • Lexicons and Emotion Mining
    • Hadoop Installation and Setup
    • Hadoop ecosystem components
    • Hadoop’s Key Characteristics
    • What is Big Data & its analytics
    • Hadoop Ecosystem and HDFS
    • Hadoop Core Components
    • Hadoop Cluster and its Architecture
    • Rack Awareness and Block Replication
    • YARN and its Advantage
    • MapReduce Framework and Pig
    • Apache Spark Next-Generation Big Data Framework
    • How Spark differs from other frameworks?
    • What is Scala
    • Scala in other Frameworks
    • Introduction to Scala REPL
    • Basic Scala Operations
    • Variable Types in Scala
    • Control Structures in Scala
    • Understanding the constructor overloading,
    • Various abstract classes
    • The hierarchy types in Scala,
    • Foreach loop, Functions and Procedures
    • Collections in Scala- Array
    • Overview to Spark
    • Spark installation, Spark configuration,
    • Spark Components & its Architecture
    • Spark Deployment Modes
    • Limitations of MapReduce in Hadoop
    • Working with RDDs in Spark
    • Introduction to Spark Shell
    • Deploying Spark without Hadoop
    • Parallel Processing
    • Spark MLLib - Modelling BigData with Spark
    • what is Kafka, Why Kafka,
    • Configuring Kafka Cluster
    • Kafka architecture
    • Producing and consuming messages
    • Operations, Kafka monitoring tool
    • Need of Apache Flume
    • What is Apache Flume
    • Understanding the architecture of Flume
    • Basic Flume Architecture
    • What is Data Visualization
    • Overview to Tableau 10.0
    • Installing Tableau, Establishing Connection
    • Tableau interface
    • Connecting to DataSource
    • Installation of Tableau Desktop
    • Architecture of Tableau
    • Connection to Excel, cubes, and PDFs
    • Data extraction, Data blending
    • Calculations to your workbook
    • Mapping data in Tableau
    • Custom Geocoding, Polygon Maps.
    • Web Mapping Services.
    • Background Images.
    • Dashboards and Stories

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