Data Analyst Skillpath: Excel, SQL & ML with Python in हिंदी

Destiny For Everything


Study knowledge analytics by studying Excel, SQL, Python, Analytics & ML ideas from scratch in Hindi

What you’ll be taught

Microsoft Excel सीखने वाले सभी नए विद्यार्थियों के लिए कोर्स – इसमें आप Microsoft Excel, Spreadsheets, Formulation, Excel shortcuts, Macros आदि सीखेंगे

Excel के सबसे ज़रूरी और लोकप्रिय Lookup फंक्शन जैसे – Vlookup, Hlookup, Index और Match फंक्शनों को काफी अच्छे से सीख पाओगे ।

SQL की सभी ज़रूरी instructions को सीख पाओगे |

Bar chart, Scatter plots, Histogram आदि का उपयोग करके अपने दर्शकों को आकर्षित कर पाओगे ।

Machine Studying Linear Regression downside के लिए knowledge assortment और knowledge preprocessing की गहराई से जानकारी प्राप्त करोगे |

Description

You’re searching for an entire course on how one can grow to be a knowledge analyst, proper?

You’ve discovered the appropriate Knowledge Analyst Masterclass with Excel, SQL & Python course! This course will educate you data-driven decision-making, knowledge visualization, knowledge analytics in SQL, and using predictive analytics like linear regression in enterprise settings.

After finishing this course it is possible for you to to:

  • Grasp Excel’s hottest lookup capabilities reminiscent of Vlookup, Hlookup, Index, and Match
  • Change into proficient in Excel knowledge instruments like Sorting, Filtering, Knowledge validations, and Knowledge importing
  • Make nice shows utilizing Bar charts, Scatter Plots, Histograms, and so on.
  • Change into proficient in SQL instruments like GROUP BY, JOINS, and Subqueries
  • Change into competent in utilizing sorting and filtering instructions in SQL
  • Discover ways to clear up real-life enterprise issues utilizing the Linear Regression approach
  • Perceive how one can interpret the results of the Linear Regression mannequin and translate them into actionable perception

How this course will allow you to?

A Verifiable Certificates of Completion is introduced to all college students who undertake this course on Knowledge Analyst Skillpath in Excel, SQL, and Python.

If you’re a scholar, enterprise supervisor, or enterprise analyst, or an government who needs to be taught Knowledge Analytics ideas and apply knowledge analytics strategies to real-world issues of the enterprise perform, this course offers you a stable base for Knowledge Analytics by instructing you the most well-liked knowledge evaluation fashions and instruments

Why do you have to select this course?

We consider in instructing by instance. This course isn’t any exception. Each Part’s major focus is to show you the ideas by way of how-to examples. Every part has the next parts:

  • Ideas and use instances of various Statistical instruments required for evaluating knowledge analytics fashions
  • Step-by-step directions on implementing knowledge analytics fashions
  • Downloadable recordsdata containing knowledge and options used within the course
  • Class notes and assignments to revise and follow the ideas

The sensible lessons the place we create the mannequin for every of those methods are one thing that differentiates this course from every other course out there on-line.

What makes us certified to show you?

The course is taught by Abhishek (MBA – FMS Delhi, B. Tech – IIT Roorkee) and Pukhraj (MBA – IIM Ahmedabad, B. Tech – IIT Roorkee). As managers within the World Analytics Consulting agency, we have now helped companies clear up their enterprise issues utilizing Analytics and we have now used our expertise to incorporate the sensible elements of enterprise analytics on this course. Now we have in-hand expertise in Enterprise Evaluation.

We’re additionally the creators of a number of the hottest on-line programs – with over 1,200,000 enrollments and hundreds of 5-star critiques like these ones:

This is superb, i like the very fact the all clarification given could be understood by a layman – Joshua

Thanks Writer for this glorious course. You’re the greatest and this course is price any value. – Daisy

Our Promise

Instructing our college students is our job and we’re dedicated to it. When you have any questions in regards to the course content material, follow sheet, or something associated to any matter, you possibly can at all times submit a query within the course or ship us a direct message.

Obtain Follow recordsdata, take Quizzes, and full Assignments

With every lecture, there are class notes hooked up so that you can observe alongside. It’s also possible to take quizzes to examine your understanding of ideas like Knowledge Analytics in MS Excel, SQL, and Python. Every part comprises a follow task so that you can virtually implement your studying on Knowledge Analytics.

What is roofed on this course?

The evaluation of information shouldn’t be the primary crux of analytics. It’s the interpretation that helps present insights after the appliance of analytical strategies that makes analytics such an necessary self-discipline. Now we have used the most well-liked analytics software program instruments that are MS Excel, SQL, and Python. This can assist the scholars who don’t have any prior coding background to be taught and implement Analytics and Machine Studying ideas to truly clear up real-world issues of Knowledge Evaluation.

Let me offer you a short overview of the course

  • Half 1 – Excel for knowledge analytics

Within the first part, i.e. Excel for knowledge analytics, we’ll discover ways to use excel for data-related operations reminiscent of calculating, remodeling, matching, filtering, sorting, and aggregating knowledge.

We will even cowl how one can use several types of charts to visualise the info and uncover hidden knowledge patterns.

  • Half 2 – SQL for knowledge analytics

IN the second part, i.e. SQL for knowledge analytics, we might be instructing you all the pieces in SQL that you’ll want for Knowledge evaluation in companies. We’ll begin with fundamental knowledge operations like making a desk, retrieving knowledge from a desk and so on. In a while, we’ll be taught superior matters like subqueries, Joins, knowledge aggregation, and sample matching.

  • Half 3 – Preprocessing Knowledge for ML fashions

On this part, you’ll be taught what actions you want to take step-by-step to get the info after which put together it for evaluation, these steps are essential. We begin with understanding the significance of enterprise data then we’ll see how one can do knowledge exploration. We discover ways to do univariate evaluation and bivariate evaluation then we cowl matters like outlier therapy, lacking worth imputation, variable transformation, and correlation.

  • Half 4 – Linear regression mannequin for predicting metrics

This part begins with easy linear regression after which covers a number of linear regression.

Now we have coated the essential idea behind every idea with out getting too mathematical about it so that you just perceive the place the idea is coming from and the way it is necessary. However even for those who don’t perceive it, it is going to be okay so long as you discover ways to run and interpret the outcome as taught within the sensible lectures.

I’m fairly assured that the course offers you the mandatory data on Knowledge Evaluation, and the skillsets of a Knowledge Analyst to right away see sensible advantages in your office.

Go forward and click on the enroll button, and I’ll see you in lesson 1 of this Knowledge Analyst Skillpath course!

Cheers

Begin-Tech Academy

हिन्दी
language

Content material

Introduction

Introduction
Course Assets

Excel Fundamentals

Fundamentals
Milestone!
Worksheet Fundamentals
Knowledge Codecs
Knowledge Dealing with Fundamentals – Lower, Copy and Paste
Saving and Printing – Fundamentals

Important Formulation

Fundamental Method Operations
Mathematical Features
Distinction between RANK, RANK.AVG and RANK.EQ
Textual Features
Logical Features
Date-Time Features
Lookup Features (V Lookup, Hlookup, Index-Match)

Knowledge Instruments

Knowledge Instruments
Formatting knowledge and tables

Excel Charts

Significance of information visualization
Components of charts
The Simple means of making charts
Bar and column charts
Formatting Charts
Line Charts
Space Charts
Pie and Doughnut Charts
Scatter plot or XY chart
Waterfall Charts
Sparklines

Pivot desk and Pivot charts

Pivot Tables
Pivot Charts

Macros

Macros

SQL Introduction

Introduction

Set up and getting began

Set up
If pgAdmin shouldn’t be opening…

Case research

Case Examine half:1
Case Examine half: 2

Basic SQL statements

CREATE
Train 1 Create DB and Desk
Options to all Workouts
INSERT
Import knowledge from File
Train 2 Inserting and Importing
SELECT assertion
SELECT DISTINCT
WHERE
Logical operators
Train 3 SELECT WHERE
UPDATE
DELETE
ALTER
Train 4 Updating Desk

Restore and Again-up

Restore and Again-up
Debugging restoration points
Creating DB utilizing CSV recordsdata
Debugging abstract and Code for CSV recordsdata
Train 5 Restore and Again-up

Part instructions: Filtering

IN
BETWEEN
LIKE
Train 6: In, Like & Between

Choice instructions: Ordering

Aspect Lecture Commenting in SQL
ORDER BY
LIMIT
Train 7 Sorting

Alias

AS

Combination Instructions

Depend
SUM
AVERAGE
MIN MAX
Train 8 Combination capabilities

Group By Instructions

GROUP BY
HAVING
Train 9 Group By

Conditional Assertion

CASE WHEN

JOINS

Introduction to Joins
Ideas of Becoming a member of and Combining Knowledge
Making ready the info
Internal Be part of
Left Be part of
Proper Be part of
Full Outer Be part of
Cross Be part of
Intersect and Intersect ALL
Besides
Union
Train 10 Joins

Subqueries

Half-1 Subquery in WHERE clause
Half-2 Subquery in FROM clause
Half-3 Subquery in SELECT clause
Train 11 Subqueries

Views and Indexes

VIEWS
INDEX
Train 12 Views

String Features

LENGTH
UPPER LOWER
REPLACE
TRIM LTRIM RTRIM
CONCATENATION
SUBSTRING
LIST AGGREGATION
Train 13 String Features

Mathematical Features

CEIL FLOOR
RANDOM
SETSEED
ROUND
POWER
Train 14 Mathematical Features

Date-Time Features

CURRENT DATE TIME
AGE
EXTRACT

Knowledge Sort conversion capabilities

Changing Numbers Date to String
Changing String to Numbers Date

Introduction to Linear Regression

Welcome to the module

Organising Python and Jupyter Pocket book

Putting in Python and Anaconda
Opening Google colab
Arithmetic operators in Python Python Fundamentals
Strings in Python Half 1
Lists Tuples and Directories Half 1
Working with Numpy Library of Python
Working with Pandas Library of Python
Working with Seaborn Library of Python

Fundamentals of Statistics

Forms of Knowledge
Forms of Statistics
Describing knowledge Graphically
Measures of Facilities
Measures of Dispersion

Introduction to Machine Studying

Introduction to Machine Studying

Knowledge Preprocessing

Gathering Enterprise Information
Knowledge Exploration
The Dataset and the Knowledge Dictionary
Importing Knowledge in Python
Univariate evaluation and EDD
EDD in Python
Outlier Therapy
Outlier Therapy in Python
Lacking Worth Imputation
Lacking Worth Imputation in Python
Seasonality in Knowledge
Bi-variate evaluation and Variable transformation
Variable transformation and deletion in Python
Non-usable variables
Dummy variable creation Dealing with qualitative knowledge
Dummy variable creation in Python
Correlation Evaluation
Correlation Evaluation in Python

Linear Regression

The Downside Assertion
Fundamental Equations and Peculiar Least Squares (OLS) methodology
Assessing accuracy of predicted coefficients
Assessing Mannequin Accuracy RSE and R squared
Easy Linear Regression in Python
A number of Linear Regression
The F – statistic
Decoding outcomes of Categorical variables
A number of Linear Regression in Python
Take a look at-train break up
Bias Variance trade-off
Extra about test-train break up
Take a look at prepare break up in Python
Linear fashions aside from OLS
Subset choice strategies
Shrinkage strategies Ridge and Lasso
Ridge regression and Lasso in Python
The ultimate milestone!

Congratulations & about your certificates

Bonus Lecture

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