What is Data Analysis? Why Advance Excel & Python are Essential for Data Analysis Courses?
Data analysis or Data Analytics is a process of inspecting, cleansing, transforming, and modeling data. The goal is to discover useful information, inform conclusions, and support decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Python Programming is essential for Data Analysis. You will learn to create Dashboard, MIS System, and many more things in this data analysis course. This course will help you to create -Dashboard (MIS System)
Python is a valuable part of the data analyst’s toolbox, as it’s tailor-made for carrying out repetitive tasks and data manipulation, and anyone who has worked with large amounts of data knows just how often repetition enters into it. By having a tool that handles the grunt work, the data analysts are free to handle the more interesting and rewarding parts of the job. Join Python Classes in Surat to learn a data analysis course. Python libraries like NumPy, Pandas, and Matplotlib, help the data analyst carry out his or her functions
Microsoft Excel is one of the top tools for data analysis and the built-in pivot tables are arguably the most popular analytic tool. In addition, Excel formulas can be used to aggregate data to create meaningful reports. Proper Knowledge of advanced Excel is important to learn Data Analysis, Join Excel Training Institute near me. You can create MIS System using Advance Excel
What you will learn in Data Analysis Course?
What is Data Analytics?
Business Analytics with Excel
Gain a foundational understanding of business analytics using Excel
Master SQL concepts such as Universal Query Tool and SQL command
Understand the nuances of lists, sets, dictionaries, conditions and branching, objects, and classes in Python
Work with data in Python, including reading and writing files, loading, working, and saving data with Pandas
Programming Basics and Data Analytics with Python
Data Science with R Programming
different types of data analysis and the key steps in a data analysis process
learn about the different types of data structures, file formats, sources of data, and the languages data professionals use in their day-to-day tasks.
process and steps involved in identifying, gathering, and importing data from disparate sources.
How to create MIS System (Dashboard)
Frequently Asked Questions about Data Analysis Course
Q – Is the course for data analysts the same as for data scientists?
A – Data Analyst will have an intermediate level of learning path whereas, the Data Scientist will have an advanced level of learning path.
Q – Can a person with no basic computer programming knowledge learn to be a data analyst by enrolling in a course?
A – No, the Student must have Basic knowledge of Computer basic
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Curriculum
- 17 Sections
- 315 Lessons
- 34 Weeks
- DATA ANALYSIS IN EXCEL51
- 1.1Quick review on MS Excel Options, Ribbon, Sheets
- 1.2Saving Excel File as PDF, CSV and Older versions
- 1.3Using Excel Shortcuts with Full List of Shortcuts
- 1.4Copy, Cut, Paste, Hide, Unhide, and Link the Data in Rows, Columns and Sheet
- 1.5Using Paste Special Options
- 1.6Formatting Cells, Rows, Columns and Sheets
- 1.7Data Validation in Excel
- 1.8Grouping & Subtotal in Excel
- 1.9Protecting & Unprotecting Cells, Rows, Columns and Sheets with or without Password
- 1.10Page Layout and Printer Properties
- 1.11Working with Formulas / function
- 1.12Logical Function: IF / ELSE, AND, OR, NOT, TRUE, NESTED IF/ELSE etc
- 1.13Database Functions
- 1.14Date & Time Functions: DATE, DATEVALUE, DAY, DAY360, SECOND, MINUTES, HOURS, NOW, TODAY, MONTH, YEAR, YEARFRAC, TIME, WEEKDAY, WORKDAY
- 1.15Information Functions
- 1.16Math & Trigonometry Functions: RAND, ROUND, CEILING, FLOOR, INT, LCM, EVEN, SUMIF,
- 1.17Statistical Functions: AVEDEV, AVERAGE, AVERAGEA, AVERAGEIF, COUNT, COUNTA, COUNTBLANK, COUNTIF, MAX, MAXA,MIN, MINA, STDEVA
- 1.18Text Functions: LEFT, RIGHT, TEXT, TRIM, MID, LOWER,UPPER, PROPER, REPLACE, REPT, FIND, SEARCH,SUBSTITUTE, TRIM, TRUNC, CONVERT, CONCATENATE.
- 1.19Conditional Formatting
- 1.20Using Conditional Formatting
- 1.21Using Conditional Formatting with Multiple Cell Rules
- 1.22Using Color Scales and Icon Sets in Conditional Formatting
- 1.23Creating New Rules and Managing Existing Rules
- 1.24Data Lock & Protection
- 1.25Advance Charts in Excel
- 1.26Area Charts and Surface Charts
- 1.27Trend line Charts and Candle Stick
- 1.28Charts and Pie Charts
- 1.29XY (Scatter Charts)
- 1.30Time Series Charts and Bubble Charts
- 1.31Radar Charts and Doughnut Charts
- 1.32Rotating 3D Excel Charts
- 1.33Working with Fill Series & Go to Special
- 1.34Consolidation in Excel
- 1.35What if Analysis in Excel
- 1.36Goal Seek
- 1.37Scenario Manager
- 1.38Data Table
- 1.39Working with Histogram
- 1.40Helps in summarize discrete or continuous data
- 1.41Helps in identifying the most efficient pricing plans in sales & marketing
- 1.42Regression Analysis in Excel
- 1.43Used To Analyse Categorical Data
- 1.44Commonly Used In Understanding Customer Behaviour
- 1.45To Understand the Relationship between a Company’s Stock Price & The Company’s Quarterly Earnings
- 1.46Working With Forecasting
- 1.47Sales Forecasting
- 1.48Demand Forecasting
- 1.49Forecasting For Decision Making
- 1.50Know How To Capture Information About Significant Market Events.
- 1.51Solving Complex Data Using The Power Query In Excel
- Professional Dashboards In Excel5
- Database Design in SQL27
- 3.1Introduction
- 3.2What is a Data Warehouse?
- 3.3Structure of a Data Warehouse
- 3.4Star Schema
- 3.5OLAP vs. OLTP
- 3.6Creating 1 dimensional arrays
- 3.7Creating 2 dimensional arrays
- 3.8Array indexing
- 3.9Accessing array elements
- 3.10Concatenating Numpy arrays
- 3.11Arithmetic operations with arrays
- 3.12Covariance
- 3.13Correlation
- 3.14Linear Regression
- 3.15Overview of various methods and attributes of Pandas
- 3.16Introduction
- 3.17Working with various Series attributes
- 3.18Introduction
- 3.19Pivoting
- 3.20Sorting
- 3.21Aggregation
- 3.22Descriptive statistical analysis with Pandas
- 3.23Introduction to Matplotlib
- 3.24Various Matplotlib methods
- 3.25Creating Line, Scatter, Bar, etc. charts
- 3.26Customising charts using: X and Y labels, Limits, Ticks, Legends
- 3.27Introduction to Pyplot
- Querying in MySQL16
- 4.1Introduction
- 4.2Creating New Tables
- 4.3SQL SELECT Statements
- 4.4Manipulating Data(Insert, Delete, Update)
- 4.5Distinct, Order By, Join clauses and Aggregate Functions
- 4.6Using Primary keys, Foreign keys
- 4.7Get Data from Multiple Tables
- 4.8Using DDL Statements
- 4.9Restricting and Sorting Data
- 4.10Using Single-row Functions
- 4.11Conversion Functions
- 4.12Conditional Expressions
- 4.13Using the Group Functions
- 4.14Subqueries to solve queries
- 4.15Linking SQL file with Python
- 4.16Accessing database
- Joins and Set Operations in SQL7
- Basic Python26
- 6.1Introduction to Python
- 6.2Python – The Universal Language
- 6.3Installing Python
- 6.4iPython – a better Python interpreter
- 6.5Types – Dynamic v/s Static Typing – tstrong v/s Weak Typing
- 6.6Numbers
- 6.7Strings
- 6.8Unicode
- 6.9Complex Types
- 6.10Operators – Operator Overloading
- 6.11Variables
- 6.12Scopping And Expressions
- 6.13Use of tabs and whitespaces as indent
- 6.14Conditionals – for…else
- 6.15The general syntax
- 6.16Default values for arguments
- 6.17Returning and receiving multiple values
- 6.18Variable number of arguments – args, kwargs
- 6.19Scope revisited
- 6.20Primitive v/s Composite Types
- 6.21Lists
- 6.22Tuples
- 6.23Maps (or Dictionaries)
- 6.24Sets
- 6.25Enums
- 6.26Looping Techniques
- Python Numpy9
- Python Pandas11
- Python Matplotlib11
- 9.0Introduction to Data Visualisation with Matplotlib
- 9.1Introduction to Matplotlib
- 9.2The Necessity of Data Visualisation
- 9.3Visualisations – Some Examples Facts and Dimensions
- 9.4Bar Graph
- 9.5Scatter Plot
- 9.6Line Graph and Histogram
- 9.7Outliers Analysis with Boxplots
- 9.8Subplots
- 9.9Choosing Plot Types
- 9.10Project
- Python Seaborn13
- 10.0Introduction
- 10.1Distribution Plots & Styling Options
- 10.2Pie – Chart and Bar Chart
- 10.3Scatter Plots & Pair Plots
- 10.4Revisiting Bar Graphs and Box Plots
- 10.5Heatmaps
- 10.6Line Charts
- 10.7Stacked Bar
- 10.8Charts Case Study Summary
- 10.9Plotly Practice Questions
- 10.10Practice Questions Solution
- 10.11Data Visualisation Practice Questions
- 10.12Case Study
- Statistics Essentials11
- Microsoft Power BI31
- 12.1COMPONENTS OF POWER BI: DESKTOP, SERVICE, AND MOBILE APPS
- 12.2BENEFITS AND APPLICATIONS OF POWER BI
- 12.3POWER BI ARCHITECTURE OVERVIEW
- 12.4INSTALLING AND SETTING UP POWER BI DESKTOP
- 12.5CONNECTING TO DATA SOURCES (EXCEL, CSV, ONLINE SERVICES, ETC.)
- 12.6CLEANING AND TRANSFORMING DATA
- 12.7HANDLING MISSING DATA SPLITTING AND MERGING COLUMNS
- 12.8DATA FORMATTING AND STANDARDIZATION WORKING WITH RELATIONSHIPS BETWEEN TABLES
- 12.9CREATING CUSTOM COLUMNS AND MEASURES WITH DAX (DATA ANALYSIS EXPRESSIONS)
- 12.10CREATING RELATIONSHIPS BETWEEN TABLES UNDERSTANDING
- 12.11CALCULATED COLUMNS AND MEASURES
- 12.12MANAGING MODEL PERFORMANCE WITH OPTIMIZATION TECHNIQUES
- 12.13CREATING BASIC CHARTS: BAR, LINE, PIE, AND COLUMN CHARTS
- 12.14ADVANCED VISUALS: SCATTER PLOTS, MAPS, FUNNEL CHARTS, & GAUGES USING SLICERS, FILTERS, & DRILL-THROUGHS.
- 12.15CREATING AND CUSTOMIZING DASHBOARDS FORMATTING AND STYLING REPORTS FOR BETTER USER EXPERIENCE USING POWER BI MARKETPLACE FOR CUSTOM VISUALS
- 12.16CALCULATED COLUMNS AND MEASURES
- 12.17AGGREGATION FUNCTIONS (SUM, AVERAGE, COUNT)
- 12.18TIME INTELLIGENCE FUNCTIONS (YTD, MTD, QTD)
- 12.19PUBLISHING REPORTS TO POWER BI SERVICE
- 12.20CREATING DASHBOARDS IN POWER BI SERVICE
- 12.21SHARING REPORTS AND DASHBOARDS WITH OTHERS
- 12.22EXPORTING REPORTS TO PDF
- 12.23POWER BI FOR BUSINESS USE CASES
- 12.24SALES ANALYSIS
- 12.25FINANCIAL REPORTING
- 12.26CONNECTING POWER BI DESKTOP WITH MOBILE FOR REAL TIME ANALYSIS
- 12.27CASE STUDIES AND LIVE PROJECTS
- 12.28RETAIL ANALYSIS DASHBOARDS
- 12.29TELECOM CHURN RATE DASHBOARD
- 12.30OLA COMPANY DASHBOARD
- 12.31BLINKIT ANALYSIS DASHBOARD
- Git and GitHub3
- R Programming43
- 14.1R–OVERVIEW
- 14.2R – ENVIRONMENT SETUP
- 14.3R – BASIC SYNTAX
- 14.4R – DATA TYPES
- 14.5R – VARIABLES
- 14.6R – OPERATORS
- 14.7R – DECISION MAKING
- 14.8R – LOOPS
- 14.9R – FUNCTION
- 14.10R – STRINGS
- 14.11R – LISTS
- 14.1213.R – MATRICES
- 14.13R – ARRAYS
- 14.14R – FACTORS
- 14.15R – DATA FRAMES
- 14.16R – PACKAGES
- 14.17R – DATA RESHAPING Melt
- 14.18The Data
- 14.19R – CSV FILES
- 14.20R – EXCEL FILE Input as xlsx
- 14.21File
- 14.22R – BINARY FILES
- 14.23R – XML FILES
- 14.24R – JSON FILE
- 14.25R – WEB DATA
- 14.26R – DATABASES
- 14.27R – PIE CHARTS
- 14.28R – BOXPLOTS
- 14.29R – HISTOGRAMS
- 14.30R – LINE GRAPHS
- 14.31R – SCATTERPLOTS
- 14.32R – LINEAR REGRESSION
- 14.33R – MULTIPLE REGRESSION
- 14.34R – LOGISTIC REGRESSION
- 14.35R – NORMAL DISTRIBUTION
- 14.36R – BINOMIAL DISTRIBUTION
- 14.37R – POISSON REGRESSION
- 14.38R – ANALYSIS OF COVARIANCE
- 14.39R – TIME SERIES ANALYSIS
- 14.40R – NONLINEAR LEAST SQUARE
- 14.41R – DECISION TREE
- 14.42R – SURVIVAL ANALYSIS
- 14.43R – CHI SQUARE TEST
- Understanding EDA36
- 15.1The CRISP-DM Framework
- 15.2DEFINE THE BUSINESS PROBLEM – BUSINESS UNDERSTANDING
- 15.3OWNING AN IPL TEAM – BUSINESS UNDERSTANDING
- 15.4UNDERSTANDING RAW DATA
- 15.5PREPARING DATA FOR ANALYSIS
- 15.6DATA ANALYSIS: MODELLING
- 15.7MODEL EVALUATION
- 15.8MODEL DEPLOYMENT
- 15.9DATA SOURCING
- 15.10PUBLIC AND PRIVATE DATA
- 15.11DATA CLEANING
- 15.12FIXING ROWS AND COLUMNS
- 15.13MISSING VALUES
- 15.14STANDARDISING VALUES
- 15.15FILTERING DATA
- 15.16INVALID VALUES
- 15.17DATA DESCRIPTION
- 15.18UNIVARIATE ANALYSIS
- 15.19UNORDERED CATEGORICAL VARIABLES
- 15.20QUANTITATIVE VARIABLES – SUMMARY METRICS
- 15.21QUANTITATIVE VARIABLES – UNIVARIATE ANALYSIS
- 15.22SEGMENTED UNIVARIATE
- 15.23INTRODUCTION TO SEGMENTED UNIVARIATE ANALYSIS
- 15.24BASIS OF SEGMENTATION
- 15.25QUICK WAY OF SEGMENTATION
- 15.26COMPARISON OF AVERAGES
- 15.27COMPARISON OF OTHER METRICS
- 15.28BIVARIATE ANALYSIS
- 15.29BIVARIATE ANALYSIS ON CONTINUOUS VARIABLES
- 15.30BUSINESS PROBLEMS INVOLVING CORRELATION
- 15.31BIVARIATE ANALYSIS ON CATEGORICAL VARIABLES
- 15.32DERIVED METRICS
- 15.33WHAT ARE DERIVED METRICS?
- 15.34TYPE DRIVEN METRICS
- 15.35BUSINESS DRIVEN METRICS
- 15.36TYPES OF DERIVED METRICS: DATA DRIVEN
- CASE STUDIES8
- EDA PROJECTS7
Siddharth Parakh
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