DATA SCIENCE Online Training

Lucidtechsystems is a brand and providing quality Online, Classroom, Corporate, Weekends and Fast track training on demand to the students in worldwide on DATA SCIENCE Training from Hyderabad. Lucidtechsystems is one best DATA SCIENCE Training institute in Hyderabad and providing IT End-to-End training with real-time hands-on exposure through the IT Experts to the students. We are giving best on DATA SCIENCE online Training and offline training in Hyderabad. Lucidtechsystems provides flexibility to the students while choosing online classes, classroom training, corporate training, summary of courses and their scope.

Data Science Training Faculty : Real-time experience

Methodology:

Lucidtechsystems Manage risk and reduce compliance costs with end-to-end processes for compliance, governance and risk. Class discussion, class assignments, A/V presentation, real-life experience, classroom exercises, and self-study.

Highlights In Our DATA SCIENCE  Training Service:

In Lucidtechsystems all faculties or trainers have experienced the pleasure of training and trained Resources is accessible all over the world. Lucidtechsystems Training leads to excel understanding, IT Industry latest skills requirement and proficiency with consulting service Lucidtechsystems real-time skilled instructors accomplish your ideas/thoughts and create competently /expertly driven environment. We will develop the association with DEVELOPMENT, TESTING, QA, UAT, STAGE and PRODUCTION Environments.

Lucidtechsystems help out in certification programs , Resume preparation, Boost Interview skills, Live Project life cycle, evolution, clarifying Doubts through Questions and Answers Method, Providing the Material with Video Sessions, troubleshooting bugs or issues in different target environment such as  DEVELOPMENT, TESTING, QA, UAT, STAGE and PRODUCTION, support the novel thoughts.

Lucidtechsystems is one best on DATA SCIENCE  Training through online and offline or classroom training center in Hyderabad , We also provide on DATA SCIENCE online Training in  India,  Chennai, Bangalore, Mumbai, Calcutta, Pune, Delhi, Noida, gorgon, NCR region, USA, UK, Canada, Japan, Malaysia, Singapore, UAE, Kuwait , Abudhabi, Dubai, Australia, South Africa etc.

Also, see Business Analyst Training.

Course Content:

GETTING STARTED WITH GITHUB

  • Introduction to Git
  • Introduction to Github
  • Creating a Github Repository
  • Basic Git Commands
  • Basic Markdown

  GETTING STARTED WITH R

  • Overview of R
  • R data types and Objects
  • Getting Data In and Out of R
  • Subsetting R Objects
  • Dates and Times

GETTING STARTED WITH R

  • Control structures
  • Functions
  • Scoping rules of R
  • Coding Standards for R
  • Dates and times

GETTING STARTED WITH R

  • Loop Functions
  • Vectorizing a Function
  • Debugging
  • Profiling R Code
  • Simulation

DATA EXTRACTION, PREPARATION AND MANIPULATION ( R, MYSQL, HDFS, HIVE AND SQOOP)

DATA EXTRACTION

  • Downloading Files
  • Reading Local Files
  • Reading Excel Files
  • Reading JSON
  • Reading XML
  • Reading From WEB
  • Reading From API

DATA EXTRACTION

  • READING FROM HDFS
  • READING FROM MYSQL
  • SQOOP
  • READING FROM HIVE
  • SAVING AND TRANSPORTING OBJECT
  • READING COMPLEX STRUCTURE

DATA PREPARATION

  • Subsetting and Sorting
  • Summarizing Data
  • Creating New Variable
  • Regular Expression
  • Working With Dates

DATA MANIPULATION

  • Managing DataFrame with dplyr package
  • Reshaping Data
  • Merging Data

DESCRIPTIVE STATISTICS

  • Univariate Data and Bivariate Data
  • Categorical and Numerical Data
  • Frequency Histogram and Bar Charts
  • Summarizing Statistical Data
  • Box Plot, Scatter Plot, Bar Plot, Pie Chart

      PROBABILITY

  • Conditional Probability
  • Bayes Rule
  • Probability Distribution
  • Correlation vs Causation
  • Average
  • Variance
  • Outliers
  • Statistical Distribution
  • Binomial Distribution
  • Central Limit Theorem
  • Normal Distribution
  • 68-95-99.7 % Rule
  • Relationship Between Binomial and Normal Distribution

HYPOTHESIS TESTING

  • Hypothesis Testing
  • Case Studies

INFERENTIAL STATISTICS

  • Testing of Hypothesis
  • Level of Significance
  • Comparison Between Sample Mean and Population Mean
  • z- Test
  • t- Test

ANOVA (F- TEST)

  • ANCOVA
  • MANOVA
  • MANCOVA

REGRESSION AND CORRELATION

  • Regression
  • Correlation
  • CHI-SQUARE

PRINCIPAL OF ANALYTIC GRAPH

INTRODUCTION TO GGVIS

  • Exploratory and Explainatory
  • Design Principle
  • Load ggvis and start to explore
  • Plotting System in R
  • ggvis – graphics grammar

LINES AND SYNTAX

  • Properties for Lines
  • Properties for Points
  • Display Model Fits

TRANSFORMATIONS

  • ggvis and dplyr

HTMLWIDGET

  • Geo-Spatial Map
  • Time Series Chart
  • Network Node

PREDICTIVE MODELS AND MACHINE LEARNING ALGORITHM – SUPERVISED REGRESSION

REGRESSION ANALYSIS

  • Linear Regression
  • Non- Linear Regression
  • Polynomial Regression
  • Curvilinear Regression

MULTIPLE LINEAR REGRESSION

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

LOGISTIC REGRESSION

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

TIME SERIES FORECAST

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

PREDICTIVE MODELS AND MACHINE LEARNING ALGORITHM – SUPERVISED CLASSIFICATION

NAÏVE BAYES

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

SUPPORT VECTOR MACHINE

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

RANDOM FOREST

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

K- NEAREST NEIGHBORS

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

CLASSIFICATION AND REGRESSION TREE (CART)

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

PREDICTIVE MODELS AND MACHINE LEARNING ALGORITHM – UNSUPERVISED

K MEAN CLUSTER

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

APRIORI ALGORITHM

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

CASE STUDY : CUSTOMER ANALYTIC – CUSTOMER LIFETIME VALUE

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

TEXT MINING, NATURAL LANGUAGE PROCESSING AND SOCIAL NETWORK ANALYSIS

NATURAL LANGUAGE PROCESSING

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

SOCIAL NETWORK ANALYSIS

  • Collect Data
  • Explore and Prepare the data
  • Train a model on the data
  • Evaluate Model Performance
  • Improve Model Performance

CAPSTONE PROJECT

  • Saving R Script
  • Scheduling R Script

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