What is Data Analysis ? Why Advance Excel & Python are Essential for Data Analysis Course?
Data analysis or Data Analytics is a process of inspecting, cleansing, transforming, and modeling data. The goal is to discovering useful information, informing conclusions, and supporting 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 Dash board , MIS System and many more things in this data analysis course. This course will help you to create -Dash board (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 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 advance 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 abut Data Analysis Course
Q – Is the course for data analyst same as data scientist?
A – Data Analyst will have the intermediate level of learning path whereas, the Data Scientist will have the advance 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 , Student must have Basic knowledge of Computer basic
Click here to explore our advance Excel Course Details
Course Features
- Lectures 167
- Quizzes 0
- Duration 24 week
- Language English and Hindi
- Students 281
- Certificate No
- Assessments Yes
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Module 1
- Introduction to MS Excel, Quick review on MS Excel Options, Ribbon, Sheets
- Difference between Excel 2003, 2007, 2010 and 2013
- Saving Excel File as PDF, CSV and Older versions
- Using Excel Shortcuts with Full List of Shortcuts
- Copy, Cut, Paste, Hide, Unhide, and Link the Data in Rows, Columns and Sheet
- Using Paste Special Options
- Formatting Cells, Rows, Columns and Sheets
- Protecting & Unprotecting Cells, Rows, Columns and Sheets with or without Password
- Page Layout and Printer Properties
- Inserting Pictures and other objects in Worksheets
- Lookup and Reference Functions: VLOOKUP, HLOOKUP, INDEX, MATCH, etc
- Database Functions
- Date and Time Functions: DATE, DATEVALUE, DAY, DAY360, SECOND, MINUTES, HOURS, NOW, TODAY, MONTH, YEAR, YEARFRAC, TIME, WEEKDAY, WORKDAY etc
- Statistical Functions: AVEDEV, AVERAGE, AVERAGEA, AVERAGEIF, COUNT, COUNTA, COUNTBLANK, COUNTIF,FORECAST, MAX, MAXA,MIN, MINA, STDEVA etc
- Text Functions: LEFT, RIGHT, TEXT, TRIM, MID, LOWER, UPPER, PROPER, REPLACE, REPT, FIND, SEARCH, SUBSTITUTE, TRIM, TRUNC, CONVERT, CONCATENATE, DOLLAR etc And More
- Using Conditional Formatting
- Using Conditional Formatting with Multiple Cell Rules
- Using Color Scales and Icon Sets in Conditional Formatting
- Creating New Rules and Managing Existing Rules
- Sorting Data A-Z and Z-A
- Using Filters to Sort Data
- Advance Filtering Options
- Creating Pivot Tables
- Using Pivot Table Options
- Changing and Updating Data Range
- Formatting Pivot Table and Making Dynamic Pivot Tables
- Creating Pivot Charts
- Types of Pivot Charts and Their Usage
- Formatting Pivot Charts and Making Dynamic Pivot Charts
- Advanced Filtering
- Data validation
- Consolidation
- Groups
- Subtotal
- How to Record / Run Excel Macro ?
- What is Excel Macro ?
- How Add Developer’s Tab in Excel 2007 / 2010
- How to add different types of controls like Text Box, Radio button, button etc. in Excel
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Module 2
- Mathematical Function (Using Case Studies)
- Logical Function (Using Case Studies)
- Text Function (Using Case Studies)
- Flash Fill (Using Case Studies)
- Area Charts
- Surface Charts
- Trend line Charts
- Candle Stick Charts
- Pie Charts
- XY (Scatter Charts)
- Time Series Charts
- Bubble Charts
- Radar Charts
- Doughnut Charts
- Rotating 3D Excel Charts
- More Case Studies
- Add-On Tools (Analytical Tools)
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Basic Python
- Introduction to Python
- Python – The Universal Language
- Installing Python
- iPython – a better Python interpreter
- Types – Dynamic v/s Static Typing – tstrong v/s Weak Typing
- Numbers
- Strings
- Unicode
- Complex Types
- Operators – Operator Overloading
- Variables
- Scopping And Expressions
- Use of tabs and whitespaces as indent
- Conditionals – for…else
- The general syntax
- Default values for arguments
- Returning and receiving multiple values
- Variable number of arguments – args, kwargs
- Scope revisited
- Primitive v/s Composite Types
- Lists
- Tuples
- Maps (or Dictionaries)
- Sets
- Enums
- Looping Techniques
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Data Analysis with Python
- Installing Anaconda and Jupyter Notebook
- Jupyter Notebook overview
- Importing data from a csv, text file or another database
- Overview of various methods and attributes of Numpy
- Array
- Creating 1 dimensional arrays
- Creating 2 dimensional arrays
- Array indexing
- Accessing array elements
- Concatenating Numpy arrays
- Arithmetic operations with arrays
- Covariance
- Correlation
- Linear Regression
- Overview of various methods and attributes of Pandas
- Introduction
- Working with various Series attributes
- Introduction
- Pivoting
- Sorting
- Aggregation
- Descriptive statistical analysis with Pandas
- Introduction to Matplotlib
- Various Matplotlib methods
- Creating Line, Scatter, Bar, etc. charts
- Customising charts using: X and Y labels, Limits, Ticks, Legends
- Introduction to Pyplot
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R Programming
- R–OVERVIEW
- R – ENVIRONMENT SETUP
- R – BASIC SYNTAX
- R – DATA TYPES
- R – VARIABLES
- R – OPERATORS
- R – DECISION MAKING
- R – LOOPS
- R – FUNCTION
- R – STRINGS
- R – LISTS
- 13.R – MATRICES
- R – ARRAYS
- R – FACTORS
- R – DATA FRAMES
- R – PACKAGES
- R – DATA RESHAPING Melt
- The Data
- R – CSV FILES
- R – EXCEL FILE Input as xlsx
- File
- R – BINARY FILES
- R – XML FILES
- R – JSON FILE
- R – WEB DATA
- R – DATABASES
- R – PIE CHARTS
- R – BOXPLOTS
- R – HISTOGRAMS
- R – LINE GRAPHS
- R – SCATTERPLOTS
- R – LINEAR REGRESSION
- R – MULTIPLE REGRESSION
- R – LOGISTIC REGRESSION
- R – NORMAL DISTRIBUTION
- R – BINOMIAL DISTRIBUTION
- R – POISSON REGRESSION
- R – ANALYSIS OF COVARIANCE
- R – TIME SERIES ANALYSIS
- R – NONLINEAR LEAST SQUARE
- R – DECISION TREE
- R – SURVIVAL ANALYSIS
- R – CHI SQUARE TEST
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SQL
- Introduction
- Creating New Tables
- SQL SELECT Statements
- Manipulating Data(Insert, Delete, Update)
- Distinct, Order By, Join clauses and Aggregate Functions
- Using Primary keys, Foreign keys
- Get Data from Multiple Tables
- Using DDL Statements
- Restricting and Sorting Data
- Using Single-row Functions
- Conversion Functions
- Conditional Expressions
- Using the Group Functions
- Subqueries to solve queries
- Linking SQL file with Python
- Accessing database
