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Best Data Analytics Using Python Training Courses in Ghaziabad

Data is produced at an alarming rate every minute. Businesses, however, are trying to take advantage of every possibility to understand the significance of the data.

This is why Data analytics is now crucial for running a business efficiently. It is used extensively in firms to boost profits and business growth.

Learn how Data Analytics with Python Certificate gives benefits to you.

Data Analysis has been in use for quite a while. Until recently, the developers used expensive and closed-source tools such as Tableau.

However, in recent years, Python, SQL, and other open-source libraries have revolutionised Data Analysis forever.

With the Data Analysis using Python certification, you'll master the basics of data analysis using Python.

After completing this certification, you'll know how to extract data from sources like CSVs and SQL and use libraries such as Numpy, Pandas, Matplotlib, and Seaborn for processing and presenting data.

Why Data Analytics Using Python?

There are numerous programming languages; however, Python is a popular choice for data analysis for engineers, statisticians, and scientists.

Here are a few reasons why Data Analytics Training using Python Programming has been gaining popularity:

  • Python is easy to master and comprehend and has a simple syntax.
  • It is a programming language that is adaptable and adaptable. It has a vast collection of mathematical computation libraries and manipulating data.
  • Python has data and graphics visualisation libraries that allow you to create plots.
  • It is well-supported by the community to assist with various issues.

Python Libraries for Data Analytics

One of the primary reasons for Data Analytics using Python Training being the most sought-after and widely used method for data analytics is that it offers various libraries.


NumPy supports n-dimensional arrays and offers tools for numerical computing. It can be helpful in Linear algebra and Fourier transformation.


Pandas provide functions to manage missing data, carry out mathematical operations, and alter the data.


Matplotlib library is widely used to plot data points and create interactive visualisations of data.


SciPy library is utilised for scientific computing. It includes modules for Optimization Integration.

  • Linear Algebra Interpolation
  • Specialised Functions
  • Image and Signal Processing

Scikit-Learn Library

Scikit-Learn has capabilities that allow you to construct regression, classification, and clustering models.

What is Data Analytics?

Analytics using data can be described as investigating large data sets and analysing them to predict trends and improve the power of data-driven decision-making.

Data analytics lets us gather, clean, and transform data into valuable knowledge. It can help answer questions, test hypotheses, or disprove theories.

Let's check out the different applications of data analytics….

Applications of Data Analytics

Data analytics is utilised in all sectors of the business. Here are a few areas where data analytics can work its work:

  • Data analytics training is utilised in banking and e-commerce to identify fraudulent transactions.
  • The healthcare sector uses data analytics to enhance patient health by detecting illnesses before occurrence. This is typically employed to detect cancer.
  • Data analytics is a tool used in inventory management to keep track of various things.
  • Logistics companies employ data analytics to speed up product delivery by optimising the route of vehicles.
  • Marketing professionals use analytics to communicate with the appropriate customers and carry out targeted marketing to boost the return on investment.
  • One can use data analytics in city planning to create intelligent cities.

Types of Data Analytics

Data analytics Courses in Ghaziabad can broadly be divided into three kinds:

  • Descriptive Analytics

It reveals what happened. One can accomplish this through exploratory data analysis.

  • Predictive Analytics

It informs you of what is likely to take place. One can accomplish this through the development of predictive models.

  • Prescriptive Analytics

It will help you create something that happens. It is possible to accomplish this by extracting crucial insights and hidden patterns from the information.

The graphical presentation illustrates the difficulty level and values obtained from different data analyses.

Data Analytics Process Steps

There are five main phases involved in the data analysis process. They comprise:

Data Collecting

The primary stage in data analysis is gathering or collecting relevant information from different sources. Data may come from various databases or log files, web servers, social media, Excel, and CSV files.

Prepare the Data

The second step involves preparing the data. This consists of purifying the data to eliminate irrelevant and unnecessary values, then converting it into the proper format and making it suitable for analysis. This also involves data processing.

Data Exploration

When the data is compiled, one can explore various visualisation techniques to uncover undiscovered trends.

Data Modelling

Your following step is to create models that predict, employing machine learning algorithms to forecast the future.

Interpretation of Results

The last stage of any data analytics procedure is to draw meaningful conclusions and verify that the outputs are consistent with the expected results.

How Can Python Be Used to Perform Data Analytics?

Python is a well-known multi-purpose programming language extensively employed for its versatility and its extensive library of libraries useful for analysis and complex calculations.

The extensibility of Python means it can use hundreds of libraries dedicated to analytics, such as the well-known Data Analytics Using Python Library.

In most cases, Data analytics libraries available for Python are, at the very least, developed from NumPy Library. This library includes hundreds of mathematical operations, calculations, and functions.

Python analytics tools are now famous thanks to their widespread acceptance and flexibility in creating multi-faceted solutions.

Its status as an all-purpose language implies that it can add more excellent capabilities to software for data analytics than domain-specific languages, which are limited in function and scope.

Furthermore, the performance of Python is superior to other languages used for data analytics. Its ability to work with a broader range of languages makes it more efficient in most situations.

Python's relatively low usage of processors and memory means it can rapidly outperform programs like MatLab or R, explicitly designed for statistical analysis.

How Can Data Analytics Use Python Program Works?

Various ways exist to integrate Python Data Analytics Certification in Ghaziabad into existing business intelligence and analytics tools.

One of the most popular applications of Python is its capacity to design and manage data structures rapidly.

Pandas, for instance, provide various tools to manipulate analysis, analyse, and even create complex data structures and sets.

This can include time series and more complicated data structures such as pivoting, merging and slicing tables to provide new perspectives and perspectives on the existing set.

Additionally, Scikit-Learn offers advanced analytics tools paired with advanced machine learning features.

This allows you to create more advanced models and perform more complicated multivariate regressions and data processing.

In conjunction with libraries like iPython and NumPy, these tools could form the foundation of an effective data analytics system.

In addition, you can use Python to develop your algorithms for data analysis, which can be integrated directly into your tools for business intelligence via API.

Learn Data Analytics with Python Certification Course in Ghaziabad

The use and production of data are increasing, and all businesses, regardless of size or turnover, are today more dependent on data than ever before. This blog post provides the basics of Data Analytics with Python Training Courses in Ghaziabad, and Python Programming Language has proven efficient in this area.

This has increased the need for Data Analytics, and many professionals are drawn to this area.

They are turning to program languages such as R and Python to increase their Analytical data abilities, but frequently don't succeed because of their flawed learning methods.

Python is an object-oriented programming system with many tools and libraries that simplify Data Analysis work.

This is why there is increasing demand for Python among Data Analysts and Data Scientists.

The article will also detail the steps to follow if you want to learn at the beginning and gain proficiency in data analytics using Python.

Additionally, it will highlight the most common mistakes you must avoid during this learning journey.

Overview of Data Analytics with Python Training Institute in Ghaziabad

Data Analytics is gathering data from many sources and then applying Statistical Analysis and Machine Learning to that data to obtain necessary information.

It's a well-known concept, particularly in the commercial industry, since it allows businesses to make decisions based on Data Analysis.

At present, Data Analytics and Python are two distinct terms. Python has gained popularity in the area of Data Science and Data Analytics due to its incredible flexibility and capabilities.

In addition, you don't need to know everything you can about programming to implement Data Analytics with Python.

Since you're not developing anything, knowing the various functions and libraries Python provides suffices.

Furthermore, you need to build your Data Science skills; otherwise, learning Python is as if you had a tool but could not use it.

Thus, you should learn Statistics and Data Visualisation skills and acquire a certain amount of understanding about the data to be gathered and analysed.

How to Master Data Analytics Using Python Training in Ghaziabad?

Data Analytics can appear to be an intimidating process for those new to the field. Still, you will be able to comprehend the fundamentals of the implementation of Data Analytics with Python by using the steps below:

# Step 1 Python Environment Set Up

The most fundamental requirement to work within Data Analytics with Python Institute Ghaziabad is to have a platform where it is possible to write your code and then execute it.

Therefore, the first task is to establish your environment in a way that's simple to use and allows you to work with Python.

Many online platforms give you the necessary programming environment, the most well-known of which is the Anaconda Python platform.

This program will handle most of your requirements since it comes with the fundamental Python Programming Language and, most importantly, libraries like Pandas, Numpy, Matplotlib, IPython, etc.

Download this Anaconda Package and install it on your system like other applications. It has various built-in applications, one of which is the Jupyter Notebook. Jupyter Notebook.

It is a highly well-designed environment for working with Python, allowing you to run and seamlessly compile your code.

Jupyter Notebook will be accessible within your browser and doesn't require an internet connection to run your code. Once the installation is complete, the environment is now all set!

# Step 2 Comprehensive Fundamental Concepts

It is crucial to begin by understanding the fundamentals of Python before engaging in any Data Analytics with Python.

You don't have to be an expert in this programming language; a brief overview of the following subjects will suffice.

  • Implementing Data Structures
  • Learning the Various Data Types
  • Creating Functions
  • Using Loops
  • Using Conditional Statements
  • Working with Imports

In addition, you don't have to take any classes to understand those concepts.

It is accessible online and offers detailed instructions on Python fundamentals through notes, videos, etc.

Understanding these concepts will give you the necessary foundation to begin the journey to Data Analytics with Python.

Step 3 # Working on Python Libraries

One of the main advantages of Python is the availability of many libraries that will make your life more accessible to a significant degree. If you are planning to do Data Analytics with Python Courses Ghaziabad, it is essential to familiarise yourself with the most widely utilised Python Libraries.

The most critical Python Libraries about Data Science are:

  • Pandas- Data Framing Structure

It's the most crucial Python Library for Data Manipulation and Data Analysis.

Including Data Manipulation tools and high-level Data Structures makes it perfect for Data Cleaning, Data Manipulation, and Data Analysis, which are essential tasks for every Data Analyst.

It has a Data Structure known as a Data Frame that is exceptionally useful for storing data in tabular format.

Pandas also allow you to tidy up your data, fill in any gaps, and apply different elements of Data Preprocessing.

  • Numpy- Influential Computation Tools

The Numpy Python Library offers powerful computational tools that simplify your Mathematical and Statistical operations when implementing Data Analytics with Python Certification in Ghaziabad.

Numpy is the fundamental Python Library. Pandas can be described as an expansion of Numpy and are only.

The primary reason for Numpy's scientific computing speed is that it includes Multidimensional arrays designed explicitly for calculations used within Machine Learning algorithms.

  • Scikit-learn- Getting Useful Insights

This is the choice for Python Library when you wish to build any Machine Learning model.

Scikit-learn can simplify obtaining valuable insights from a vast volume of data if you're implementing Data Analytics using Python Programming.

Furthermore, it lets users build models that use Machine Learning algorithms to predict the future and produce results.

This library is also perfect for Data Mining, providing a user-friendly interface for various Machine Learning models.

  • Matplotlib- Visualised Presentation

This library includes capabilities to visualise your data using different graph-based representations.

Matplotlib gives you complete control over the graphs.

You can alter the colours Shapes, Shapes, Axis style, thickness, range, etc., that you want to change in the visual plot.

These four Python Libraries are necessary should you want to get involved in Data Analytics with Python.

Once you've understood these, you can look into other libraries to expand your knowledge of using Data Analytics with Python.

These libraries and more are already installed within the Jupyter Notebook. Installing a library with the command pip is easy if it is not installed.

Step 4 # Work using Datasets

The previous three steps focused on learning specific techniques and tools to help you with your Data Analytics using Python. Now, it's time to apply this knowledge using actual datasets.

Plenty of Datasets are available within the Stats Models Library for Python, and you can download additional data on platforms to test your skills further.

When you apply the basic Statistical and Analytical procedures to these Datasets, you will gain faith in Data Analytics, and Python will improve.

You can identify the areas you're required to work on. With these Datasets, you should apply the following four types of techniques:

  • Data Cleaning involves finding and correcting errors or misinformation in the stored data.
  • Data Preprocessing is changing data into formats better suited to perform Data Analytics with Python.
  • Data Manipulation is applying Machine Learning models to data to achieve the desired results. Tasks like Clustering, Classification, Regression, etc., are all part of Data Manipulation.
  • Data visualisation is the results obtained through any of the three methods of Data Analytics that use Python and are displayed more comprehensibly. It includes Bar Graphs, Pie Charts, Heat Maps, etc., as illustrated in the following image.

Beware of the Common Mistakes in Data Analytics using Python.

The majority of beginners make the following errors while trying to learn Data Analytics with Python:

Start Learning the Theory of Learning Excessive about Language Coding and Data.

When they first start learning Data Analytics with Python Training Institute, many people tend to concentrate on the technical elements of Python. They focus on learning the theory behind Machine Learning algorithms rather than engaging in practical work.

This theory-based approach will make learning more complex and intimidating for certain people. This could cause you to quit earlier in your learning.

In learning complex Algorithms at an early stage, in The first stage, there is no need to master complex Machine Learning algorithms.

People new to the field often believe that mastering various complex algorithms early can give them a competitive advantage.

This is a mistake to avoid since it won't allow you to establish a solid Python Programming Language base.

Rather than experimenting with different algorithms, knowing the best algorithm to use in what contexts is crucial.

Avoid committing these errors; concentrate on taking time and learning at your own pace.

Also, it would be best to focus on the practical application that uses Python Libraries and simple Machine Learning algorithms.

Note to Be Pointed…..

It outlined the steps you must follow to succeed when using Data Analytics & Python Training Courses in Ghaziabad. Additionally, it identified the frequent mistakes to avoid when beginning your learning journey.

While data Analytics is a complicated field, Python's built-in libraries and functions will simplify it.

All you have to follow is the instructions provided in this article and try to practice the best you can.

Benefits of Taking Admission to Best Data Analytics with Python Training Institute

Data analytics is the application of data science to business problems. The goal is to use data analysis and machine learning to extract meaningful insights from large amounts of complex data.

  • Data is information that can be used for predictions and descriptions.
  •  Data analytics can be understood by considering how you might use this approach to analyse financial statements, track traffic patterns, and predict customer behaviour.

This data analytics Using Python course in Ghaziabad will provide a solid foundation for a data analytics career.

It includes principles like data visualisation, data mining, and classification. Learners can identify and use different data types such as text, numeric, time series, and definite.

These topics will give learners hands-on experience building practical applications. They also learn how to create and implement models in their practice sessions.

Growing Culture of Data Analytics and Python Programming

Since the dawn of time, data analytics has rapidly grown. Data analytics is now a vital tool for businesses in all industries.

Companies use this tool to learn about customers and offer their needed services.

Many companies even offer their own data analytics tools. Data analytics is now an essential skill for any business professional.

Although we think of data analytics with Python programming as a tool for large corporations, it can also be helpful for individuals. You might learn data analytics if you want to start your own business. You will know what data analytics is and how to get started.

To learn data analytics, you must find out if any training programs can teach you. Python training institute Ghaziabad offers a variety of courses, including data analytics.

Data analytics can bring many benefits. Data analytics can be used to spot patterns and trends in data. It will be possible to spot correlations between data sets based on specific parameters.

This can help you predict the future. These insights will allow you to make better business decisions and move faster.

Every course offered by the IT training institute in Delhi NCR gives students hands-on training in data analytics skills.

As mentioned, data analytics can also provide many other benefits. These benefits include understanding customer behaviour and building better products through market research.

They also help you identify bottlenecks in your processes, which can be used to improve them.

It should come as no surprise that almost anyone can benefit from learning Data Analytics Using Python. If you're interested in learning data analytics, don't wait! Get started today!

Overview of the Course Softcrayons Best Data Analytics with Python Training Institute

Learn to analyze data with Python. This course will guide you through the basics of Python fundamentals and explore the many kinds of data.

Learn how to analyze data, prepare it for analysis, carry out fundamental statistical analyses, make valuable data visualizations, forecast the future of data trends, and more.

Learn how to:

  • Import data sets
  • Clean and prepare the data to analyze
  • Manipulate pandas Dataframe
  • Summarise data
  • Create machine learning models with sci-kit-learn
  • Develop data pipelines

Data Analysis using Python is taught via lectures, hands-on labs, and assignments. It consists of the following components:

The Data Analysis library taught me how to work with Pandas Data Frames, Numpy multidimensional arrays, and SciPy library to deal with different datasets.

In this course, we will introduce pandas, an open-source library, and then utilise it to load the data, manipulate, analyse and visualise excellent data.

Then, we will introduce you to an open-source library called Scikit-learn, and we will use one of its machine-learning algorithms to create intelligent models and astonishing predictions.

Softcrayons is the best IT Training Institute based in Ghaziabad. You can join the league to stand out among the crowd. The student will get the best professional training, making it more authentic.

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  • Introduction To Python
  • Why Python
  • Application areas of python
  • Python implementations
  • Cpython
  • Jython
  • Ironpython
  • Pypy
  • Python versions
  • Installing python
  • Python interpreter architecture
  • Python byte code compiler
  • Python virtual machine(pvm)

  • Using interactive mode
  • Using script mode
  • General text editor and command window
  • Idle editor and idle shell
  • Understanding print() function
  • How to compile python program explicitly

  • Character set
  • Keywords
  • Comments
  • Variables
  • Literals
  • Operators
  • Reading input from console
  • Parsing string to int, float

  • If statement
  • If else statement
  • If elif statement
  • If elif else statement
  • Nested if statement

  • While loop
  • For loop
  • Nested loops
  • Pass, break and continue keywords

  • Int, float, complex, bool, nonetype
  • Str, list, tuple, range
  • Dict, set, frozenset

  • What is string
  • String representations
  • Unicode string
  • String functions, methods
  • String indexing and slicing
  • String formatting

  • Creating and accessing lists
  • Indexing and slicing lists
  • List methods
  • Nested lists
  • List comprehension

  • Creating tuple
  • Accessing tuple
  • Immutability of tuple

  • How to create a set
  • Iteration over sets
  • Python set methods
  • Python frozenset

  • Creating a dictionary
  • Dictionary methods
  • Accessing values from dictionary
  • Updating dictionary
  • Iterating dictionary
  • Dictionary comprehension

  • Defining a function
  • Calling a function
  • Types of functions
  • Function arguments
  • Positional arguments, keyword arguments
  • Default arguments, non-default arguments
  • Arbitrary arguments, keyword arbitrary arguments
  • Function return statement
  • Nested function
  • Function as argument
  • Function as return statement
  • Decorator function
  • Closure
  • Map(), filter(), reduce(), any() functions
  • Anonymous or lambda function

  • Why modules
  • Script v/s module
  • Importing module
  • Standard v/s third party modules
  • Why packages
  • Understanding pip utility

  • Introduction to file handling
  • File modes
  • Functions and methods related to file handling
  • Understanding with block

  • Need of regular expressions
  • Re module
  • Functions /methods related to regex
  • Meta characters & special sequences

  • Database Concepts
  • What is Database Package?
  • Understanding Data Storage
  • Relational Database (RDBMS) Concept

  • lSQL basics
  • DML, DDL & DQL
  • DDL: create, alter, drop
  • SQL constraints: Not null, unique, Primary & foreign key, composite key Check, default
  • DML: insert, update, delete and merge
  • DQL : select
  • Select distinct
  • SQL where
  • SQL operators
  • SQL like
  • SQL order by
  • SQL aliases
  • SQL views
  • SQL joins
  • Inner join
  • Left (outer) join
  • Right (outer) join
  • Full (outer) join
  • My SQL functions
  • String functions
  • Char_length
  • Concat
  • Lower
  • Reverse
  • Upper
  • Numeric functions
  • Max, min, sum
  • Avg, count, abs
  • Date functions
  • Curdate
  • Curtime
  • Now

  • Difference between list and numpy array
  • Vector and matrix operations
  • Array indexing and slicing

  • Labeled and structured data
  • Series and dataframe objects

  • How to load datasets
  • From excel
  • From csv
  • From html table

  • at & iat
  • loc & iloc
  • head() & tail()

  • describe()
  • groupby()
  • crosstab()
  • boolean slicing / query()
  • Data Manipulation & Cleaning
  • Map(), apply()
  • Combining data frames
  • Adding/removing rows & columns
  • Sorting data
  • Handling missing values
  • Handling duplicacy
  • Handling data error

  • lScatter plot, lineplot, bar plot
  • Histogram, pie chart
  • Jointplot, pairplot, heatmap
  • Outlier detection using boxplot

  • Dynamic Array, Array Function, Month Names, Size of an Array.
  • Function and Sub: User Defined Function, Custom Average Function, Volatile Functions, ByRef and ByVal.
  • Application Object: Status Bar, Read Data from Text File, Write Data to Text File.
  • ActiveX Controls: Text Box, List Box, Combo Box, Check Box, Option Buttons, Spin
  • Button, Loan Calculator.

  • User form and Ranges, Currency Converter, Progress Indicator, Multiple
  • List Box Selections, Multicolumn Combo Box, Dependent Combo Boxes
  • Loop through Controls, Controls Collection

  • Tableau – Home
  • Tableau – overview
  • Tableau - environment setup
  • Tableau - get started
  • Tableau – navigation
  • Tableau - design flow
  • Tableau - file types
  • Tableau - data types
  • Tableau - show me
  • Tableau - data terminology

  • Tableau - custom data view
  • Tableau - data sources
  • Tableau - extracting data
  • Tableau - fields operations
  • Tableau - editing metadata
  • Tableau - data joining
  • Tableau - data blending

  • Tableau - add worksheets
  • Tableau - rename worksheet
  • Tableau - save & delete worksheet
  • Tableau - reorder worksheet
  • Tableau - paged workbook

  • Tableau – operators
  • Tableau – functions
  • Tableau - numeric calculations
  • Tableau - string calculations
  • Tableau - date calculations
  • Tableau - table calculations
  • Tableau - lod expressions

  • Tableau - basic sorting
  • Tableau - basic filters
  • Tableau - quick filters
  • Tableau - context filters
  • Tableau - condition filters
  • Tableau - top filters
  • Tableau - filter operations

  • Tableau - bar chart
  • Tableau - line chart
  • Tableau - pie chart
  • Tableau – crosstab
  • Tableau - scatter plot
  • Tableau - bubble chart
  • Tableau - bullet graph
  • Tableau - box plot
  • Tableau - tree map
  • Tableau - bump chart
  • Tableau - gantt chart
  • Tableau – histogram
  • Tableau - motion charts
  • Tableau - waterfall charts
  • Tableau – dashboard

  • One project using python & sql
  • One dashboard using tableau

  • 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- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
  • Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas etc)

  • Importing Data from various sources (CSV, txt, excel, access, etc)
  • Database Input (Connecting to the database)
  • Viewing Data objects – subsetting, methods
  • Exporting Data to various formats
  • Important python functions: Pandas

  • Course Introduction
  • Data Analytics Overview
  • Statistical Computing
  • Mathematical Computing Using NumPy
  • Data Manipulation with Pandas
  • Data visualization with Python
  • Intro to Model Building

  • R Basics
  • Data Structures in R
  • R Programming Fundamentals
  • Working with Data in R
  • Stings and Dates in R
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