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Data Science Training

overview

A Data Analyst, as a rule, clarifies what is happening by preparing a history of the information. Then again, Data Scientist not exclusively does the exploratory examination to find experiences from it, yet in addition, utilizes different propelled AI calculations to recognize the event of a specific occasion later on. A Data Scientist will take a gander at the information from numerous edges, once in a while edges not known before. Information Science is a mixture of various devices, calculations, and AI standards with the objective to find concealed examples from the crude data. How is this not the same as what analysts have been getting along for a particular amount of time? The appropriate response lies in the contrast among clarifying and anticipating. ITcources.com provides you the best Data Science online training or online classes in the Bangalore for the certification.

What do you do with Data science ?

So, before being prepared for handling, all information experiences pre-handling. This is a fundamental gathering of tasks that convert crude information into an arrangement that is progressively reasonable and consequently, helpful for additional handling. Regular procedures are:

Gather crude information and store it on a server

This is immaculate information that researchers can’t examine straight away. This information can emerge out of studies, or through the more well known programmed information assortment worldview, similar to treats on a site.

Class-name the perceptions

This comprises of orchestrating information by class or marking information that focuses on the right information type. For instance, numerical, or straight out.

Information purifying/information scouring

Managing conflicting information, as incorrectly spelled classes and missing qualities.

Information adjusting

On the off chance that the information is lopsided with the end goal that the Data Science Online Training classes contain an inconsistent number of perceptions and are in this manner not delegate, applying information adjusting techniques, such as removing an equivalent number of perceptions for every classification, and setting up that for handling, fixes the issue.

Information rearranging

Re-masterminding information focuses to wipe out undesirable examples and improve prescient execution further on. This is applied when, for instance, if the initial 100 perceptions in the information are from the initial 100 individuals who have utilized a site; the information isn’t randomized, and designs because of examining rise.

What is Data science certification?

It’s unmistakable that Data Science has an exceptionally encouraging future and has a great deal of extension. There is an enormous deficiency of HR right now, particularly in India; it is evaluated that by 2019, there will be a setback of 1.5 million information researchers. Remembering this, the two understudies and experts are for the most part ready to have an edge over every other candidate on the off chance that they influence their degree or certification on the equivalent. A portion of the courses worth referencing is:

Coursera-Data Science Specialization: This Specialization covers the ideas and devices you’ll require all through the whole information science pipeline, from posing the correct sorts of inquiries to making derivations and distributing results. In the last Capstone Project, you’ll apply the abilities learned by building an information item utilizing certifiable information. At the finish, understudies will have a portfolio showing their dominance of the material.

Microsoft-Professional Program for Data Science: Microsoft counseled information researchers and the organizations that utilize them to distinguish the center aptitudes they should be effective. This educated the educational plan used to show key utilitarian and specialized aptitudes, joining exceptionally evaluated Data Science online training courses with hands-on labs, deducing in the last capstone venture.

edX-Data Science Course: Multiple course programs exist to get you on a way to a vocation as an information researcher. The Micro Master’s program shows you fundamental Python programming expected to perform information assignments and investigates AI and large information examination utilizing Spark. Also, finishing a Micro Masters can kick off an information science certificate or information science aces. The Programs highlight multi-course tracks intended to give you top to bottom information and preparing. Data Science Online training and online classes are available in this certification.

What are the prerequisites for the certification?

Have a Master/Graduate Degree in any of the STEM streams.
Know the essentials of programming.
Know the fundamentals of SQL
Be comfortable with the rudiments of math’s, likelihood and measurable ideas
Direct Algebra foundation – Vectors, Matrices, and all the activities identified with it
In-depth information about the information explanatory instruments

Curriculum

Introduction to Data Science

What is Data Science?

Why Python for data science?

Relevance in industry and need of the hour

How leading companies are harnessing the power of Data Science with Python?

Different phases of a typical Analytics/Data Science projects and role of python

Anaconda vs. Python

Python Essentials (Core)

Overview of Python- Starting with Python

Introduction to installation of Python

Introduction to Python Editors

Understand Jupyter notebook & Customize Settings

Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)

Installing & loading Packages & Name Spaces

Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)

List and Dictionary Comprehensions

Variable & Value Labels – Date & Time Values

Basic Operations – Mathematical – string – date

Reading and writing data

Simple plotting

Scientific Distributions used in Python for Data Science

Numpy, scify, pandas, scikitlearn etc

Accessing / Importing and Exporting Data using Python Modules

Importing Data from various sources (CSV, txt, excel, access etc)

Database Input (Connecting to the database)

Viewing Data objects

Exporting Data to various formats

Important python modules: Pandas, selenium, pandas SQL, python integration with HTML

Data Manipulation – Cleansing – Munging using Python Modules

Cleansing Data with Python

Data Manipulation steps(Sorting, filtering, duplicates, merging, derived variables, sampling, Data type conversions, renaming, formatting etc)

Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)

Python Built-in Functions (Text, numeric, date, utility functions)

Python User Defined Functions

Normalizing data

Formatting data

Important Python modules for data manipulation (Pandas, Numpy, re, math, string, DateTime etc)

Data Analysis – Visualization using Python

Introduction exploratory data analysis

Descriptive statistics, Frequency Tables and summarization

Univariate Analysis (Distribution of data & Graphical Analysis)

Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)

Creating Graphs

Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Plotly, seaborn, bokeh, Pandas and scipy.stats etc)

Basic Statistics & Implementation of Stats Methods in Python

Basic Statistics – Measures of Central Tendencies and Variance

Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem

Inferential Statistics -Sampling – Concept of Hypothesis Testing

Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square

Important modules for statistical methods: Numpy, Scipy, Pandas

Python: Machine Learning – Predictive Modelling – Basics

Introduction to Machine Learning & Predictive Modeling

Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting

Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning

Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)

Overfitting (Bias-Variance Trade off) & Performance Metrics

Concept of gradient descent algorithm

Concept of Cross validation(Bootstrapping, K-Fold validation etc)

Model performance metrics (R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

Machine Learning Algorithms & Applications – Implementation in Python

Linear Regression Single Variable

Linear Regression Multiple Variables

Gradient descent and Cost Function

Save Model using joblib and pickle

Dummy variable and one hot encoding

Training and testing Data

Logistic Regression (Binary Classification)

Logistic Regression (Multiclass Classification)

Decision Tree

Support Vector Machine (SVM)

Random Forest

K Fold Cross Validation

K Means Clustering

Deep Learning: Tensorflow And Keras: Introduction and Installation

Tensorflow & Keras – Neural Network For Image Classification

(Tensorflow2.0, Keras & Python) – Movie Review Classification

Structured Query Language (SQL)

Introduction to MySQL

Designing Databases

Basic SQL

Database Structures

Doing Advanced Queries

Tableau

Course Introduction

Tableau Desktop

Tableau Workspace

Getting Started with Tableau

Data Types

Data Roles: Dimension vs. Measure

Data Roles: Continuous vs. Discrete

Joins in Tableau

Prepare your Data for Analysis

Filtering Data

Sorting of Data

What is a Group

Sets

Grand totals and Subtotals

Formatting and Annotations

Different Charts Tableau

Create Calculated fields and Parameters

Dashboard

Microsoft Excel

Quick Introduction

Creating Pivots

Creating Graphs

H lookup and V lookup

Basic Formulas

Data Science Training

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Curriculum

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