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Effective Uses Of Data Analytics With Python In Business

The raw data available in the world is massive. To make sense of these available statistics, data analysis comes into the picture. All organizations are trying their level best to traverse every opportunity that comes their way, in order to emerge victorious. To understand data analytics with Python, we must first know what data analytics is and what a data analyst actually does.

A guide to data analytics with python

What is Data Analysis?

Data Analytics begins by gathering all the possible raw data at a tedious pace, cleaning it, and making it ready for further analysis. This is where languages related to programming like Python and PI Data Tools flourish.

A Data Analyst takes all the raw and complex information and makes something useful out of it by interpreting these insights so that the company can make the most out of this information. Data Analytics with Python saves a lot of time.

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Application of Data Analytics With Python

The sky is the limit on this in today’s world as the amount of data available is infinite across all kinds of businesses and organizations globally. To make better and faster business decisions, reduce overall business costs, and develop innovative and user-friendly products, Data Analytics with Python is used.

Armed with all the essential data after the removal of the unnecessary ones, companies are then able to function well by making better decisions for their audience, thereby being one of the reliant industries in which they work.

Here is a guide to Data Analytics and Data Science

Types of Data Analysis

1. Descriptive Analysis- Instigates What has occurred?

Usually, a beginner is assigned with this work in the Data Analysis domain, showcasing what’s going on in the business and setting the foundation of the information relating to business. For instance, studying the total number of cosmetics sold, growth of sales, and the profit made in the past few years.

2. Diagnostic Analysis- Instigates Why it has occurred?

This analysis investigates and finds the main cause of the problem. For instance, why was there a sudden deterioration or dip in sales or why did the company’s profit go down or is the product relevant to the current trend and so on? This is where one creates the sales outlook and is considered to be one step ahead of descriptive analysis.

3. Predictive Analysis- Instigates What will occur?

The predictive analysis predicts what the company should do in the future for growth. The total units that would sell, the profit that the company can expect in the near future based on the growing trends. This analysis is important as businesses are trying to make themselves aware of the planning they need to execute to excel in their respective industries.

4. Prescriptive Analysis- Instigates How to make it occur?

Prescriptive analysis, the most advanced stage of analytics, is a combination of all the above-mentioned analyses. This stage also involves advice on what do to next. For instance, whether to launch a new product at a favorable time or make a particular investment or decision.

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The 9 Vital Tools of Data Analytics

  • Microsoft Excel is used by Data Analysts to run basic queries and to create tables, graphs, and charts.
  • Tableau is a popular Data Analytics and business intelligence software that is primarily used as a tool for data visualization. To simplify the complex and raw data into dashboards, maps, charts, and sheets, Data analysts use tableau. This helps to make the data accessible and easy to access for everybody.
  • Python is a programming language that organizes large sets of complex data. The term used in the industry to describe the processing of data in various formats is called Data Wrangling. For example, amalgamating and assembling data to get ready for inspection. Python has many built-in features which help with data wrangling making it a popular alternative to Microsoft Excel.
  • R is another programming language that is open source and used for statistical evaluation, often acting as a compatible tool with Python. R is particularly popular in data analysis because of its output. It puts forward an amazing diversity of tools for serving and conveying the outcome of data analysis.
  • SAS is a command-driven software package used for carrying out advanced statistical analysis and data visualization, offering a wide range of statistical methods and algorithms, and customizable options for analysis, output, and publication quality graphics. It is one of the most widely used software packages in the industry.

 

  • SQL stands for structured query language and its language used to access and manipulate databases. SQL is a tool that allows you to communicate and access data in a database which is necessary if you want to retrieve particular useful data for analysis. Most large businesses use some form of SQL to store their big data. So, studying SQL is vital, if your goal is to become a Data Analyst.
  • RapidMiner is a software confine used for knowledge discovery in data or uncovering patterns, data text mining, predictive analytics, and machine learning. Used by data analysts and data scientists alike, RapidMiner comes with a wide range of features including validation, data modeling, and automation.
  • Power BI is a solution related to business analytics that allows you to envision your data and share your insights across your whole organization. Similar to Tableau, Power BI is primarily used for the visualization of data, on the other hand, tableau is built for data analysts. It is a more general business intelligence tool
  • Finereport is another business intelligence tool used to monitor performance identify trends in data and create reports and dashboards. This is a user-friendly tool that is popular with both data analysts and non-data experts.

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Why Data Analytics With Python?

There are a number of reasons why people opt for  Data Analytics with python.

 

1. Easy to Grasp Due to Its Simple Syntax

Data Analytics with Python can develop codes much quicker than Java and other coding languages as it doesn’t have a very high typeset. It not just increases the ease of coding but also reduces the amount of effort needed to write codes from scratch. Python is definitely not one of those languages where one has to write thousands of lines.

Instead, it can be achieved with just minimal lines that make it extremely easy for beginners to learn the basics of python in a short period of time. Let’s illustrate with the help of an example: let us say we want to extract the first letter of the text ‘Hello World’.

The code we write for:

C# str.Substring(0,3)

JavaScript str.substr(0,3)

Python str[0:3]

Notice how short and clean the language is in Python?

 

You should know the most important Data Analyst Interview Questions

2. Higher Flexibility Than Other Programming Languages

A person can do a lot with Python due to its wide variety of programming and flexibility. It’s easily scalable as it can go from something on your machine to using a PI spark and spread it across hundreds of servers across terabytes or petabytes of data.

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3. Huge Collection of Libraries

This is interesting because Python, Java, C, and .NET have huge collections of libraries and they are always competing with one another. But Python is an open source, one can always have easy access to those libraries which otherwise wouldn’t have been available for free.

Some of the Popular Libraries in Python Are Listed Below:

●      NumPy

Its full form is numerical in Python. NumPy is a model which is an alternate format lab. It is used to create multi-dimensional arrays and calculate the matrix and it has way too many predefined mathematical functions.

●      Pandas

The panda’s library has powerful functionalities in order to process the data as it provides us with more data structures to play with. Its full form is the Python data analysis library. As the name suggests, it’s mainly used for Data Analysis. With the help of pandas, one can create a multi-dimensional structured data set.

It also helps one do some kind of processing and manipulation for the data. Similarly, data can be read from any source like a text file, a database, a CSV, PST, or JSON. Once read it, you can pass it on and convert it into a data frame so that it can be in the format of 2D.

 

●      Matplotlib

Once we are done with the data analysis, it’s time to move ahead and visualize the data and it’s done by the most popular library of Python- Matplotlib. It provides a wide variety through which one can plot the data through line graphs, bar graphs, scatter plots, pie charts, histograms, and many more.

John Hunter is the creator of Matplotlib. However, some of the segments are written in C and Javascript. The only reason is to achieve compatibility with the platform.

●      Seaborn

In order to use the Seaborn library, you first need to install it either through the installation of Anaconda or by typing Pip Install Seaborn into your command line. After the installation, you’ll need to import that library in whichever Python script you are working on. Most people use the Jupyter Notebook.

Another amazing feature of Seaborn is that it interacts easily with other libraries like Matplotlib and Pandas. Seaborn can group the data, aggregate it, and plot it all from the Pandas data frame. There are many plots that can be constructed with the help of Seaborn like KDEplot, pairplot, violinplot, boxplot, distplot, and swarmplot.


●      Scikit-learn

Scikit-learn is vital for a Python programmer, learning machine-building skills. This library simplifies the complex problems relating to machine building. In Python, Scikit-learn is known as the father of programming as it is the core library. It’s impossible for programmers to implement machine learning without the usage of Scikit-learn.

●      OpenCV

OpenCV is a library of programming functions aimed at the real-time domain of computer science which mainly deals with pictures and videos that correspond to our site. The processing of images and videos by a computer is called computer vision. Originally, OpenCV was created by Intel. This library is a cross-platform library and it is free to use under the open-source library.

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4. Python as a Great Programming Language

Python is a great language because it is a beginner language, easy to process, and debug in general. Data analytics with Python is powerful as it has great potential uses and is considered to be a universal language in terms of coding, teaching programming, web development, and game development like PyGame and Godot Engine.

Tensorflow is the largest AI framework that is possible with the help of the Python library. Tensorflow is used to program self-driving cars and other AI-based machines and devices. Also, the general machine control and science applications include control scripts for large hadron collider functions it.

 

5. Python Saves Time and Effort

If you constantly have to do repetitive and boring tasks, such as copying files and folders, renaming them, and uploading them to a server, you can easily write a Python script for automation that will eventually save you time.

For instance, if you continuously work on Excel spreadsheets, PDFs, and CSP files, download websites, and parse them, you can simply automate them with the use of Python.

A person doesn’t have to be a programmer, coder, or technologist to use Python. You can be an accountant, mathematician, or scientist and use Python to make life easier.

The Growth of Python

Python has witnessed unprecedented growth in the past few years. In 2004, it was the fourth most popular language in the world. By the month of March of the same year, it claimed the first position of being the most popular language. Forbes even reported that in 2008, the python had grown in popularity by 456%.

Many companies are now demanding data scientists who are familiar with Python. Data Analytics with Python is also extremely versatile and can be used in artificial intelligence, machine learning, web development, and nearly everything else.

 

With this versatility, companies are hiring tons of Python developers which creates a high demand for Python programmers. Besides, it is an interpreted language. It is popular because it gives access to a lot of open-source packages. A Python learner can try lots of complicated modules with just a few lines of script.

It saves a lot of time while coding because almost everything is pre-written. Most packages written for Python are already optimized. Moreover, it is the first proclaimed programming language to learn, if you are new to programming. It is fun, easy, and can land you a good salary.

Is Python Slow?

Python might definitely be slower than other programming languages, but that doesn’t make it bad because what really matters is the fact that it makes coding much easier to learn and use. Even at a beginner level, one can start building projects with the help of Python libraries to detect your voice and face.

With a wide variety of programming approaches and the use of Data Analytics with python, one can have the flexibility to choose the best approach. When it comes to CV, ML, AI, scientific computing, Arduino, Raspberry, PI, or any new technologies, one can use python, whereas other languages can and will miserably fail.

Roadmap for Python Beginners

Those wanting to learn Data Analytics with Python must be aware of the fact that it is not just an easy programming language to learn, but it is also easily accessible as it is an open-source package. Listed below are some of the various ways through which you can learn Python and become a pro in it:

  • For a beginner, just reading ‘Automate the Boring Stuff with Python’ is enough as it provides you a crystal clear view of what python actually is. This book is free and helpful for people with zero programming experience.
  • Remember that consistency is key to making Python easy to conceive. Split your Python learning into two parts. Every non-programmer needs to start by using the basic data structures. You need to be familiar with some of its libraries like pandas, NumPy, scipy and try to practice as much data as possible. After having a good grip on the basics, start learning the OOPS concept, LOOP, and even data structures using python.
  • Learn more advanced Python topics. This will definitely teach you to become a better python programmer and go more into the details with the advanced topics.
  • Contribute to open source. There can be numerous program codes that can contribute to your own features and understanding of word codes.
  • Figure out where to specialize in. python could do so much like data science, backend development, machine learning, and much more

 

  • To start with your Python learning, become familiar with Lists, Tuple, Dictionary, and Set. After the basics, spend more time with Loops (For Loop and While Loop), conditional statements (If Statement, If-else Statement, If-elif Statement), User-defined functions (to create logic and to take a lot of customized tasking), Regular Expressions, Escape Sequences, Lambda Expression, String Manipulation.
  • If your goal is to become a Data Scientist, then you need to be familiar with Python libraries like pandas, numpy, matplotlib, seaborn, sci-kit-learn, TensorFlow, NLTK, and streamlet.
  • If you want to be on an advanced level as a Python developer, then you need to be familiar with data structures and algorithms.

 

Frequently Asked Questions:

1.     Eligibility to learn Data Analytics with Python?

  • Must have a Bachelor’s Degree with at least 60% or above.
  • Graduates with Mathematics, Statistics, or Computer Science as their core subjects are highly preferable.
  • Students with inquisitive and logical skills.
  • Freshers or professionals who want a career switch.

2.     How long does it take to get a job as a Python developer?

It depends highly on your focus and consistency. Figure out where to specialize in. Be razor-sharp sure, whether you want to be a Data Analyst, Python Developer, Machine-builder, Data Engineer, or more. Once you figure out your area of interest, be determined and focused on learning the skill set you wish to acquire. You can land your dream job in nine months but that will require a lot of hard work.

3.     What salary can one expect after learning Data Analytics with Python?

If you are a fresher and have acquired the skills to be a full-stack developer (knowledge about front-end development, back-end development, data-based development, and some of the cloud management services) then your salary can start from 4.5 lakhs and go upto 7.5 lakhs. But this highly depends on how good you are with the skills. If you join any company as a normal software engineer, then the average salary is somewhere between 3 lakhs to 3.5 lakhs in the software industry which has not changed in the past five years.

4.     How can I learn data analytics with Python on my own?

  • If you want to acquire the skill set of Python programming but don’t know where to start, then you should check out some free tutorials and resources on the internet. Doing so means you won’t have to pay a single penny for any course.
  • There are many websites which are providing free courses, you will be able to acquire valuable insights and be familiar with all the basics of Python Programming completely for free.
  • These learning platforms will provide you with some coding challenges to boost your confidence, that too for free. Challenging oneself is extremely important, as it helps you to go beyond your limits as a programmer and forces you to think, not just in a creative way but also helps you to find a solution for the problem.
  • Find a community where you can ask questions and always reach out for help to those you are learning from.

Conclusion of Data Analytics with Python

Data Analysis is a booming industry and combined with the skill set of a Python developer, it just can’t get any better. One must be aware of the fact that Python is here to stay. Acquiring skills has always been proven to be a plus point and learning Data Analytics with Python will definitely keep you at the forefront, considering the growing demands for Python developers in the industry.

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