Data Science / Analysis- RS INFOTECH Computer Education #1 training institute in Vasai, Mumbai

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Data Science / Analysis | Duration :

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.

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About

Data Science / Analysis | Duration :

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.

MODULE 1 : Phyton and data Analysis
Python Essentials / Data types
Control Flow/ Class opps / File handling
Exception / Functions and MODULEs
Regular expression
MODULE 2 :Pandas
Data frame / Data summarization
Aggregation / reshaping /pivoting
Data visualization
MODULE 3 : Numpy
Numpy package/ array / N dimensional
Indexing array/ slicing/ platting / Matplotlib
MODULE 4 : Intro to Statistics
Difference between population and sample
Types of variables / Measure of central tendency
Measure of Variability / Co efficient of variance
skewness to kurtoisis
MODULE 5 : Inferential statistics
Normal Distribution /Central Limit theorem
Confidence Interval/ students T distribution
Test hypotheses / type I type II errors / T -test
MODULE 6 : My Sql
Introduction to SQL, Syntax, Select, Insert,
Update, Delect, Top, Like, Wildcard in B/W Alias,
Joins, Constraints, Create Table & Function.
MODULE 7 : Tableau
Creating Basic Visualizations
Tableau Desktop UI ,Tableau Prep Connecting to Data
Filtering and Sorting Data
Creating Groups and Hierarchies Date Functionality
Mapping, Heat Map and Highlight Table
Calculations Histogram Dashboards and Actions
Sharing your Work, Review of Tableau Prep
Review of Tableau Desktop, Creating Sets
Quick Table Calculations, Multiple Visualizations
Reference Lines, Bands & Distributions, The Power of Parameters
Motion Chart, Trends and Forecasting
Formatting Visualizations, Stories
Performance Recording, Advanced Mapping
Tableau Best Practices, Data Connections & Data Sources
Review of Tableau Desktop, Custom SQL
Context Filter, Advan

Module 9 : Exploratory data analysis
Intro to exploratory data analysis / Derive insights of data of phyton pandas
Missing value of analysis / outlier detection analysis / the correlation matrix
Module 10 : Supervised Machine learning (Artificial Intelligence AI)
Intro To Supervised Machine Learning
Linear With Regression With An Example Using Phyton Sci-kit Learn Module
Logistic With Regression With An Example Using Phyton Sci-kit Learn Module
Naive Bays Method And An Example Using Phyton Sci-kit Learn Module
Decision Tree Classifier With An Example Using Phyton Sci-kit Learn Module
Random Forest Classifier With An Example Using Phyton Sci-kit Learn Module
support Vector Machine (svm) With An Example Using Phyton Sci-kit Learn Module
Into To Neural Network / K-NN Algorithm
Module 11 : Deep Learning
Deep Learning, Deep Learning Algorithms
Installation of Keras library in Anaconda
Keras Backends, Keras Models
Keras layersKeras Models, Keras Model class
Keras Sequential classKeras Layers
Keras Core Layers, Convolutional Layer
Pooling Layers, Locally-Connected layers
Recurrent Layers, Embedding Layers
Keras Merge LayersDeep Learning Library
Deep Learning, Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
Module 12 : 'R' Language
Various Analytics tools / Data Types / Importing Data
Manipulating Data / Conditional Statements and Functions
Statistical Concepts / Predictive Modeling
Data Visualization in R using GG Plot
Misc. Functions and Data Visualization using Plotly
Advance Topics in R (Machine learning with R Concepts)
Module 13 : Data Visilition (Power BI)
What is Power BI?. Power BI Histor, Power BI C

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Data Science / Analysis | Duration :

Data Science / Analysis | Duration :

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