Making educated decisions based on data insights is becoming increasingly crucial for businesses and organizations in the age of big data. Data analytics can help with this. 

Large datasets are gathered, processed, and analyzed using data analytics to reveal important patterns and insights that can guide decision-making. Data analytics is redefining sectors and spurring innovation in everything from healthcare to finance to retail. 

But what is data analytics, and what kinds of technologies are out there? To further acquaint you with this vital subject, we’ll analyze data analytics trends in this blog and examine its definition, kinds, tools, and more.

What is Data Analytics?

 

The science of analyzing unstructured data to derive useful information is known as data analytics. Data that has a set format and is readily stored and searched in databases is called structured data. Unstructured data, like social media posts, emails, or web pages, might be more complicated and diverse since it lacks a rigid framework.

 Discover the benefits of data analytics! 

Data analytics can help you realize the value of any data, including unstructured data, sound, video, and text.

The main goal of data analytics are:

• Organizations may use data insights to inform their decisions with the use of data analytics.
• Businesses may enhance their operations and strategy by identifying patterns, trends, and opportunities through data analysis.
• Additionally, data analytics may assist companies in identifying possible hazards or difficulties before they develop into significant ones.

Data analytics ultimately aims to leverage data to spur innovation, boost productivity, and gain a competitive edge in the market.

Types of Data Analytics

According to MicroStrategy’s The Global State of Enterprise Analytics survey (pdf), 56 percent of respondents claimed data analytics resulted in “faster, more effective decision-making” at their firms. 

Other advantages include the following:

• Enhanced productivity and efficiency (64%)
• Improved financial performance (51%)
• Finding and developing new sources of revenue for goods and services (46%).
• More effective consumer acquisition and retention (46%)

• Enhanced client experiences (44%).
• Comparative advantage (43%).

Now, let’s study the different types of Data Analytics:

• Descriptive analytics: Analyzing and describing historical data to understand previous occurrences is known as descriptive analytics. Descriptive analytics, for instance, might determine which goods sold the most over the previous month or which marketing initiatives were the most effective if you manage an online store.
• Diagnostic analytics: It aims to find the underlying causes of a problem or incident. For instance, you may utilize diagnostic analytics to identify the cause of a sudden reduction in sales at your e-commerce business, such as a problem with the website or a shift in consumer behavior.
• Predictive analytics: Statistical models and machine learning algorithms are used in predictive analytics to predict upcoming events or actions. For instance, you may use predictive modeling to examine previous sales data and find patterns and trends to forecast which goods will sell the most in the upcoming month.
• Prescriptive analytics: With this form of analytics, recommendations or actions that can enhance results are made utilizing data insights. Prescriptive analytics, for instance, may be used to recommend focused marketing campaigns or adjustments to your product offers if your e-commerce company wishes to boost sales.

What are the Best Tools for Data Analytics?

Data analysts use various tools and platforms to perform data analytics tasks. 

Some of the popular data analytics tools are:

• Excel: The most well-known spreadsheet program is Excel. It also has computation and graphing tools that are excellent for data analysis. No matter your area of expertise or additional software you might want, Excel is a standard in the industry. Its useful built-in features include form design tools and pivot tables (for sorting or tallying data). 
• Python:  Python is an essential tool for every data analyst and has many applications. It places a higher priority on readability than more sophisticated languages, and because of its widespread use in the computer industry, many programmers are already familiar with it. 
• R: It is a well-known open-source programming language, much like Python. Software for statistical and data analysis is frequently made with it. Python’s syntax is simpler than R’s, but R’s learning curve is more challenging. 
• Jupyter Notebook: You may generate interactive documents using the free and open-source internet tool Jupyter Notebook. These mix live programming, arithmetic, narrative text, and images. Imagine a page similar to one from Microsoft Word that is far more dynamic and optimized for data analytics!
• Apache Spark: Data scientists and analysts can quickly examine massive data volumes using the Apache Spark software framework. In 2012, it was first developed, and after that, it was given to the charity Apache Software Foundation.

The Final Words

In today’s data-driven world, data analytics is a potent tool for businesses and organizations. Organizations may find useful insights and trends that can spur innovation, increase efficiency, and give them a competitive edge by gathering, processing, and analyzing data. 

Various methods and technologies are available to help firms make the most of their data, from descriptive analytics to prescriptive analytics. We hope this post has given you a better grasp of the definition, kinds, and tools of data analytics, whether you’re a beginner or a seasoned practitioner just beginning to explore the world of data analytics. 

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The practice of using data to maximize corporate performance and make educated decisions is known as business analytics. It entails gathering, evaluating, and interpreting information from various sources.

According to research, approximately 70% of small businesses invest more than $10,000 annually in analytics. Therefore, assist them in understanding their markets, clients, and operational procedures. 

Business analytics can help businesses improve their products and services. But how exactly does business analytics work? And what are the different types and tools of business analytics?

This blog post will explain what is business analytics, including their work and the different tools and types of business analytics available. 

What is Business Analytics?

Business analytics (BA) is the knowledge, tools, and procedures used in the iterative study and analysis of previous company performance to generate knowledge and direct business strategy. Data-driven businesses aggressively seek ways to use their data as a competitive advantage and see it as a valuable corporate asset.

Business analytics is focused on creating fresh understandings of how businesses work using data and statistical techniques. Company intelligence, in contrast, has often focused on employing consistent measures to evaluate previous performance and direct company planning. Business analytics focuses on prediction and recommendation. At the same time, business intelligence focuses on the description.

A BA background opens up a variety of job options. 

According to PayScale, some specific job titles and yearly wages as of 2021 include the following:

• A senior business analyst: $86,050

• Business systems analyst: $71,155

• Business Analyst: $69,785

• Junior business analyst: $51,009 

• Business intelligence analyst: $69,639

Explanatory and predictive modeling, numerical analysis, fact-based management, and analytical modeling are frequently used in business analytics to inform decision-making. As a result, it has a tight relationship with management science. 

Why is Business Analytics important?

Business Intelligence Analytics carries out several fundamental procedures before any data analysis is done:

• Establish the analysis’s business purpose.
• Choose an analytical strategy.
• Obtain company data from various systems and sources to assist the study.
• Cleanse and incorporate all the data into one location, such as a data warehouse or data mart.

Among the projects they could analyze are the following ones:

• Analyzing data trends to find strategic possibilities
• Recognizing potential issues the company could be experiencing and possible remedies
• Making a budget and business projection
• Tracking the success of business activities 
• Updating stakeholders on the status of business goals
• Comprehending KPIs
• Being aware of regulatory and reporting obligations

The Types of Business Analytics

Business analytics uses data to uncover patterns and support judgments in various areas, including operations, marketing, finance, and human resources. The three basic categories of business analytics are descriptive, predictive, and prescriptive.

• Descriptive analytics: It requires summarizing and visualizing historical and current data to comprehend what has occurred and what is happening in a business setting. Descriptive analytics, for instance, might be used by a business to monitor changes in website traffic, customer happiness, or sales success over time.
• Predictive analytics: Predicting future events and trends entails analyzing past and current data using statistical models and machine-learning techniques. For instance, using past data and present circumstances, a business use predictive analytics to forecast future demand, revenue, or customer attrition.
• Prescriptive analytics: It entails generating and analyzing many scenarios using optimization and simulation methods, then recommending the optimal course of action given an aim and a set of constraints. Prescriptive analytics, for instance, might be used by a business to improve its inventory levels, pricing schemes, or marketing efforts in light of its objectives and available resources.

Businesses may improve performance, make better decisions, and gain a competitive advantage using business analytics. Business analytics also needs thorough preparation, implementation, and assessment to guarantee validity, dependability, and use.

What are Business Analytics tools for small businesses?

To evaluate and analyze data, business analytics solutions gather it from one or more business systems and consolidate it in a repository, such as a data warehouse. Most businesses employ various analytics tools, including sophisticated data mining programs, spreadsheets with statistical features, and predictive modeling programs. 

The best business analytics tools give the organization a comprehensive picture of the business, revealing crucial insights and comprehension of the industry and enabling the organization to make better-informed decisions about business operations, customer conversions, and other matters.

Business analytics tools go above and beyond business intelligence tools in that they not only provide the outcomes of the data but also explain why the results happened.

Ending Note

Business analytics uses data and statistical techniques to conclude company data so that choices may be made confidently. Business analytics come in various forms, including descriptive, predictive, and Prescriptive. Several technologies, including data mining, machine learning techniques, and data visualization software, are available to execute these analytics. 

Corporate Analytics has evolved into a crucial step in the decision-making process due to the growing significance of data in today’s corporate environment. With the data science and analytics course from Imarticus Learning, which includes placement possibilities, you can unleash the potential of data analytics. Organizations may acquire a competitive edge and make wise decisions that can spur development and success by utilizing the power of data.

Python is considered the most popular programming language used by data scientists on a daily basis. As an object-oriented, high-performance, and open-source language has revolutionised solving data-related problems and tasks like data frame manipulation, data visualization, and the like. It is also widely used in multiple types of Machine Learning. Python comes with numerous useful libraries for data science that developers widely use to solve issues. 

The Python community creates and maintains these libraries, which may be installed via package managers like pip. They are simply imported into Python scripts upon installation, enabling programmers to make full use of their capabilities and features.

Why Are Python Libraries Important?

Python libraries have multiple use cases and are widely used because they are:-

• Reusable: Python libraries enable developers to reuse code developed by others to do specific tasks or address specific issues. This saves programmers a lot of time and effort because they aren’t required to write code from the scratch for each project.

• Highly efficient: Python modules are frequently optimised for speed, allowing developers to complete complicated jobs fast and efficiently. This can result in shorter development times and improved application performance.

• Standardised: Python libraries provide a consistent collection of tools and functions on which developers may rely. This makes project collaboration easy because everyone is utilising the same tools and methodologies.

• Supported: Python libraries have a huge and active community that provides assistance and contributes to their development. This can assist developers in solving difficulties fast and learning from the experiences of others.

• Innovative: Python libraries frequently provide state-of-the-art features and premium functionality that may be leveraged to develop creative apps. This can assist developers in staying miles ahead and developing solutions that satisfy changing corporate demands.

5 Most Widely Used Python Libraries 

There are dozens of readily accessible Python libraries that cover a wide variety of functionalities including data analysis, web development, scientific computing, artificial intelligence, machine learning, and others. Here is a list of the top 5 Python libraries:-

Pillow

Pillow is a well-known open-source library that enables programmers to manipulate images. It is a counterpart of PIL (Python Imaging Library) based on the OOPS concepts in programming and supports a broad range of image file formats such as GIF, JPEG, PNG, BMP, WEBP, and TIFF. It represents and manipulates pictures by using classes and objects. Developers may use Pillow to do image processing operations like cropping, filtering, resizing, and modifying colours. 

Features:-

• Image metadata support
• Easy conversion of image format
• Seamless integration with different Python libraries

Applications:-

• Image processing
• Image enhancement
• Image analysis
• Image file handling
• Web development
• Data visualization

NumPy

NumPy (Numerical Python) is the foundational Python module used in numerical computation and comprises a strong N-dimensional array object. With around 18,000 comments on GitHub, it receives a massive amount of community support via an active group of 700 contributors. It is an array-processing general-purpose software that offers high-performance arrays (multidimensional objects), and tools for manipulating them. 

Features:-

• Provides quick functions precompiled for numerical routines
• Provides better efficiency with array-oriented computing
• Encourages object-oriented strategies
• Allows for more compact and quick calculations via Vectorisation

Applications:- 

• Used widely in data analysis. 
• Generates a strong N-dimensional array.
• Formulates the foundation of different libraries like sci-kit-learn and SciPy.
• When used with SciPy and matplotlib, it helps replace MATLAB.

Pandas

Pandas (Python data analysis) is an essential component of data science and is the most popular and commonly used Python package for data research. It is widely utilised in data analysis and cleansing and is supported by an active GitHub community of around 1,200 contributors. It is popularly used for data frame manipulation and offers quick and dynamic data structures like data frame CDs, that work well with structured data. 

Features:-

• Fluent syntax and extensive functionality allow users to work with missing data.
• Allows users to write their own function and execute it on a series of data.
• A high level of abstraction
• It includes high-level data structures and tools for data manipulation.

Applications:-

• Data wrangling and cleansing
• Data frame manipulation
• ETL (extract, transform, load) processes for data transformation and storage.
• Academic and commercial applications like statistics, neurology, and economics.
• Time-series-specific functions like linear regression, moving window, date range creation, and date shifting.

Keras

Keras is a high-functioning neural network API that is written in Python and runs on top of various ML frameworks, like Theano, TensorFlow, or CNTK. It is a popular library that is widely used for various types of Machine Learning, neural network modules, and deep learning. This Python library supports the backends of both Theano and TensorFlow, making it a decent choice. 

Features:-

• An abundance of prelabeled datasets that can be used to import and load directly.
• Has a vast number of parameters and integrated layers used for building, configuring, training, and evaluating neural networks.

Applications

• Extensive creation of predictions 
• Easy extraction of characteristics
• Image classification
• Natural language processing 
• Time-series analysis
• Speech and audio recognition

Matplotlib

Matplotlib’s visualisations are both powerful and elegant. As a plotting library for Python, it has vast community support on GitHub with over 26,000 comments and over 700 developers. It is widely used for data visualisation because it helps generate graphs and plots. It also has an object-oriented API for embedding such graphs into applications. 

Features:- 

Can be used as a MATLAB substitute

• Supports dozens of backends and output types, and can be used regardless of which operating system or output format is preferred.
• Pandas may be used as MATLAB API wrappers to control MATLAB like a cleaner.
• Low memory utilisation
• Enhanced runtime performance

Applications:-

• Correlation evaluation of variables
• Display the models’ 95% confidence intervals.
• Outlier detection 
• Visualise data distribution to acquire fast insights.

Conclusion

To summarise, Python’s vast ecosystem of libraries covers a wide range of use cases, ranging from data analysis and data visualisation to ML and web development. With these libraries, developers have the ease of simply adding significant functionalities to their apps rather than implementing them from scratch.

Having in-depth knowledge of Python and its libraries is key to becoming an expert in this field. To learn more about Python libraries and their uses, you can consider joining a professional course. If you are looking for a reliable online program, you can join the course offered by Imarticus Learning. Their top-tier Postgraduate Program In Data Science And Analytics will give you the knowledge and skills necessary to move forward in this career field.

Talk of the leading global tech companies and you will see they use Python programming as an integral part of their technology stack. Created in 1991, Python has become one of the most popular programming languages worldwide. The simplicity, shorter learning curve, reduced development time and effortless coding experience has made Python a coveted choice with many developers. 

Let us see how Python is used in Data Science projects and web development along with some real-world applications. 

Using Python in Data Science projects and web development

The universal, high-level programming language, Python is used extensively in various web development and Data Science projects. 

• Web development

Flask and Django are Python frameworks, which are famous for web development. Python also has extensive modules and libraries, which speed up development time considerably. 

• Web scraping applications 

Python facilitates extracting huge volumes of data quickly from sources for price comparisons, research and development and email address gathering. With the logistic regression classification technique, Python solves classification problems. The simple-to-code programming language has a lucid syntax and a great collection of useful libraries like Pandas, Numpy and Matlplotlib. 

• Data Science

Python helps in quickly analysing and manipulating data. The programming language has graphing libraries which support data visualisation. Moreover, you will find a vibrant and active Python Data Science community. 

• Game development

Python libraries like Pygame are great for building games and prototypes. Popular games like Battlefield 2, EVE Online and World of Tanks are built with Python. 

• Python application development

Since Python is a general-purpose language, it is used for developing desktop GUIs, file directories and APIs. 

Real-world applications of Python programming

There are thousands of Python websites and apps running on the internet successfully. Let us take a look at some of the real-world applications using Python. 

Uber

Uber, the well-known mobility-as-a-service company, had doubts about choosing between Ruby and Python while selecting a programming language. They chose Python for the backend and frontend functions. 

The Uber platform needs to make many calculations. Uber’s backend predicts traffic, demand and supply, arrival times, approximate reaching time to the destination, etc. Python is also great for mathematical calculations at big data levels. 

Reddit

Do you know the internet’s popular source of cat videos or dank memes? You guessed it right – Reddit. Self-acclaimed ‘internet’s front page’, Reddit is also a great source of community interactions. 

Reddit uses Python as the programming language because of its easy readability and writeability. Moreover, Python has diverse arrays of ready-to-use libraries. Along with Python, Reddit also uses Javascript and Go. 

Spotify

As a music enthusiast, you do not need to go anywhere else other than Spotify to listen to the kind of music that you love. Spotify has developed as a huge podcast and music streaming platform with more than 489 million active users monthly worldwide. 

You don’t need to look for MP3s, torrent links, or other websites to listen to your favourite music. Spotify developers have used Python for building infrastructure to run user forecasts. 

Instagram

Python is the main programming language used in Instagram. There have been many changes in Instagram’s tech stack, but the app wouldn’t be born without Python. Instagram was built using Django, which is a Python web framework. The viral video and image-sharing platform has almost 1.35 billion users globally in 2023, which is expected to reach 1.44 billion by 2025.  

With an increasing number of users, Instagram developers are creating static sort checkers using Python for server investigation. The server has millions of lines of Python code. 

Netflix

Netflix began its business as a DVD-by-mail service. Today it has become a leading video streaming platform with millions of paid subscribers globally. One of the reasons for the popularity of Netflix is its powerful analytics and recommendation engine. The company offers suggestions to users by understanding the kind of content that they watch. The recommendation and analytics engine is based on Python. 

Extremely intuitive, Python programming language helps in solving complicated networking problems. The content lifecycle of Netflix uses Python including security tools and Machine Learning recommendation algorithms. For statistical analysis, developers use Python libraries. Python is also used for automation tasks, data cleaning and exploration and data visualisation. 

YouTube

YouTube is not only a video-streaming platform on the internet, but it is also the second-largest search engine after Google. YouTube has billions of logged-in users monthly. Along with being a search engine, YouTube is also a popular social media platform. 

YouTube is written exclusively in Python. The interactive experience that users enjoy is due to the various libraries and features of the Python language. The coding of the platform is done in a manner so that the process of downloading, uploading and sharing videos becomes easy. 

Quora

Quora is a question-and-answer platform mainly targeted at professionals who seek answers to various queries on different subjects. Quora has almost 300 million users. Along with sharing answers, professionals also share their experiences on various subjects on Quora. 

Developers of Quora tried using various programming languages for the development of the platform. Python suited them best more so because of the amazing development speed of the programming language. 

Conclusion

Python programming language is a favourite among all leading global technology leaders for its robust, reliable and engineering enterprise-level applications. The majority of websites and apps use Python for their development as the coding is simple and easy. Becoming a Python developer will help in making a great career with a lucrative pay package. 

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In today’s data-driven world, businesses are increasingly relying on analytics to gain insights and make informed decisions. One of the fundamental pillars of analytics is statistics, which involves using mathematical methods to collect, analyse, and interpret data. From predictive modelling to hypothesis testing, statistics play a crucial role in uncovering meaningful patterns and trends in data.

In addition to mathematical techniques, data visualisation is also a key component of statistical analysis, as it allows us to present complex data in a way that is easy to understand and visually appealing.

Let us explore essential statistical techniques that are commonly used in analytics and how they can enhance our understanding and interpretation of statistical results.

Essential Statistical Techniques Used in Analysis

There are a plethora of statistical techniques you can employ in order to get valuable insights from the data you possess. Down below are listed a few of them with real-world examples for each:

Descriptive Statistics

Descriptive statistics as defined by the word itself ‘describes’ the essential features that one can gather from a dataset. Let’s take a small example to understand it better.

Imagine a hypothetical scenario where a business is tasked with analysing the sales data for a product over the past year. The dataset they have at their disposal includes a wide range of variables such as the number of units sold each month, the average price per unit, and the total revenue generated.

To gain a more granular understanding of the data, the business could employ descriptive statistics techniques. This would allow them to summarise and describe key features of the dataset in a more intuitive manner.

The main techniques employed in descriptive statistics:

• Mean
• Median 
• Mode
• Variance
• Standard Deviation

Inferential Statistics

Inferential statistical techniques are employed to ‘infer’ the differences amongst groups of data and then make an assumption about the vast population pertaining to the insights gained from the inference.

Let’s say that you own a healthcare company and want to determine whether a new medication is effective in reducing blood pressure. You conduct a randomised controlled trial where you randomly assign patients to receive either the new medication or a placebo. After the trial, you collect data on the blood pressure readings for both groups. 

To draw insightful inferences about the effectiveness of the new medication, you could leverage inferential statistics techniques to analyse the data. By calculating the difference in the mean blood pressure readings between the two groups, you can gain a deeper understanding of the impact of the medication on blood pressure levels using data visualisation.

Following the inference, you could employ a hypothesis test to determine if the difference in blood pressure readings between the two groups is statistically significant. This would allow you to draw strong conclusions about the effectiveness of the new medication in reducing blood pressure.

The main techniques employed in inferential statistics:

• Hypothesis Testing
• Confidence Intervals

Correlation Analysis

Correlation Analysis is a statistical technique used to determine whether or not there is a link between two variables/datasets and the strength of that relationship.

Let’s say a company wants to investigate the relationship between advertising spend and sales revenue. They have collected an extensive dataset that contains information on the amount of money spent on advertising and the corresponding sales revenue for each month over the past year.

To unravel the intricacies of the relationship between advertising spend and sales revenue, the company could use correlation analysis techniques. This would involve calculating the correlation coefficient, which is a numerical measure that reveals the strength and direction of the linear relationship between two variables.

In the case mentioned above, the variables would be advertising spend and sales revenue. The most widely used correlation coefficients are ‘Spearman’s Rank Correlation Coefficient’ and the ‘Pearson Product-Moment Coefficient’.

If the correlation coefficient is positively skewed, it would indicate a strong positive relationship between advertising spend and sales revenue, signifying that as advertising spending increases, sales revenue also tends to increase. Conversely, if the correlation coefficient is negatively skewed, it would indicate a negative relationship, implying that as advertising spending increases, sales revenue tends to decrease.

Regression Analysis

Picture a world where you’re looking to understand the relationship between two or more variables. In walks regression analysis, a statistical method that helps you do just that. This technique is heavily utilised in an array of fields, including economics, finance, marketing, and social sciences.

It aims to pinpoint a mathematical equation that can predict the value of one variable based on the values of other variables. The variable being predicted is known as the dependent variable, while the variables that are used to predict it are known as independent variables or predictors.

Let’s say a car manufacturer wants to predict the fuel efficiency of its vehicles based on various factors such as engine size, weight, and transmission type. To achieve this, they conduct a regression analysis to identify the most significant predictors of fuel efficiency.

The manufacturer compiles data on fuel efficiency, engine size, weight, and transmission type for each of their car models. They then utilise regression analysis to construct a mathematical equation that optimally foresees fuel efficiency grounded on these variables.

Upon scrutinising the data, the regression model reveals that engine size and weight wield significant influence on fuel efficiency, whereas transmission type has no substantial impact on fuel efficiency. The car manufacturer can exploit this knowledge to reconfigure its production methods and make adjustments to the design of their cars to fine-tune fuel efficiency.

The dependent variable in the scenario above is the fuel efficiency of the vehicles. This is the variable that the manufacturer is trying to predict based on the values of the independent variables.

The independent variables are the engine size, weight, and transmission type of the vehicles. These are the variables that are used to predict the fuel efficiency of cars.

The most widely used techniques in regression analysis are:

• Linear Regression
• Logistic Regression
  NumPy, a popular Python library, is used for regression because it provides a fast and efficient array operation, mathematical functions, linear algebra operations, and interoperability with other libraries. These features of NumPy make it an ideal tool for handling the computations involved in different types of machine learning including regression modelling.
  

Conclusion

As you can understand from the aforementioned, statistical techniques play a crucial role in insights for businesses to function optimally. Though we discussed a few of the plethora of techniques, it is important to remember that these techniques are used in most types of machine learning.

If you’re interested to know more about techniques such as cluster analysis, time series analysis, and many more, then you should check out the Postgraduate Programme in Data Science and Analytics offered by Imarticus Learning. With expert instructors, hands-on projects, and a industry-relevant curriculum, this programme can help you launch your career in the dynamic field of data science. Don’t wait, click now to learn more and enrol today!

This is the age of supercomputers, Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning where computing power has gone much beyond our imagination. With such scientific and technological advancements, it has become possible to process huge volumes of data within a fraction of a second for getting valuable insights. 

A common term, which is extensively used when we talk about AI or ML is Artificial Neural Network (ANN). Artificial Neural Network is a model which imitates the way in which various nerve cells function in the human brain. There are many aspects and interesting layers in this network. Read on to learn more about Artificial Neural Networks, how they work, their advantages and other related things. 

What Are Artificial Neural Networks?

Artificial Neural Networks refer to a subfield of Artificial Intelligence, modelled after the human brain. These are algorithms which are based on brain functions for forecasting issues and modelling complicated patterns. The Deep Learning method emanates from the concept of biological neural networks. 

The main aim of developing Artificial Neural Networks was attempting to replicate the functioning of the human brain. If you study closely, you will find that the functioning of ANN is similar to that of biological neural networks. ANN algorithms work on structured and numeric data. 

The learning algorithms that ANNs use can make adjustments independently. Precisely, they can learn as they receive any kind of new input. This is the reason they are highly effective for non-linear statistical data modelling. 

The Architecture of Artificial Neural Networks

To understand Artificial Neural Networks, you must have knowledge about the architecture of ANNs.

Layers in the Artificial Neural Network

There are three or more layers in an Artificial Neural Network, which are interconnected to one another, namely the input layer, hidden layer and output layer. Because of multiple layers, ANNs are often called Multi-layer Perceptron (MLP).

The first layer has input neurons. These neurons send data to the other deeper layers in the network. After the data is processed through these layers, the final output data is sent to the last output layer. 

In the ANN, all inner layers are hidden. These layers are made of units which adaptively alter the data received from one layer to another through a long series of transformations. The hidden layer is also referred to as the ‘distillation’ layer as the most relevant and useful pattern is extracted from the inputs and sent for further analysis to the next level. Redundant information gets discarded in the process. 

Each layer in the ANN functions as both an input and output layer for understanding complex subjects well. Collectively, all the inner layers are called neural layers. 

Using backpropagation

Using backpropagation is an essential part of Artificial Neural Networks. Backpropagation is a process in which the ANN adjusts the output results by considering the errors in the account. 

During the supervised training phase, every time output is labelled as an error, it is sent backwards through the process of backpropagation. Every single weight is updated to understand how it contributed to the error. 

The error recalibrates the weight of the ANNs unit connections for understanding the difference between the actual outcome and the desired outcome. The ANN will gradually learn to minimise the chances of unwanted results and reduce the number of errors as well. 

Practical Applications of Artificial Neural Networks

Artificial Neural Networks have unique properties and therefore they are used in various applications. Some of the practical applications of Artificial Neural Networks are as follows:

• Forecasting

Artificial Neural Networks help in making forecasts which impact business decisions greatly. Regular business decisions include capacity utilisation, the financial allocation between goods, sales, etc. 

Apart from these ANNs also help in making forecasts on the stock market, monetary and economic policy, finance and other subjects. Forecasting is quite a complex thing as there are many underlying variables concerned, some of which are known and some are unknown. 

There are things that could be improved in traditional forecasting models. On the other hand, ANNs can extract previously known correlations and unknown characteristics, thus providing accurate forecasts with minimal errors. 

• Image Processing and Character Recognition

Artificial Neural Networks have a significant role in character and picture recognition. This is because these networks can take in innumerable inputs, process them and derive complicated and hidden, non-linear correlations. 

For instance, handwriting recognition is an integral part of character recognition and is used extensively in various applications like fraud detection and national-level security assessments. 

Apart from character recognition, image recognition is a fast-evolving discipline. Image recognition is now used in various applications right from cancer detection in medical science to facial identification in social media, from defence purposes to satellite image processing, agricultural purposes and so on. 

Deep Learning includes deep neural networks, which have opened up transformative and new advances in natural language processing, speech recognition and computer vision. One of the best examples in this regard is self-driving vehicles. 

Advantages of Artificial Neural Networks

Some of the most prominent advantages of Artificial Neural Networks are as follows:

• Data storage on the entire network

Data used in traditional programming is not stored on a database; but rather on the whole network. When a few pieces of data disappear in one place, it does not prevent the network from functioning. 

• Parallel processing capability 

Artificial Neural Networks have the capability of performing more than one task simultaneously. This is possible because ANNs have a numerical value. 

• ANNs can function with incomplete knowledge 

Post-ANN training, the output will be produced from the given information even with inadequate data. The loss of performance depends upon the significance of missing information. 

Summing it up

Artificial Neural Networks have myriad applications and are being extensively used in natural language processing, spam email detection, predictive analysis in business intelligence, chatbots and so on. 

A course in Data Science and Analytics helps you know more about Artificial Neural Networks. Imarticus Learning offers a Postgraduate Program in Data Science and Analytics for building a career in analytics and data science. The programme offers guaranteed job assurance. The 6 Months programme has almost 300+ learning hours with 25+ projects and 10+ tools. Give your tech career the needed boost with this course. 

Are you struggling to generate meaningful insights from the wide datasets of your enterprise for creating a business dashboard? Without the right insights, your decision-making will become vulnerable. 

Implementing Microsoft Power BI is the best solution to this issue. The drag-and-drop options, simple user interface and not requiring coding make Power BI a favourite tool for building business dashboards and data visualisation. 

Building an excellent dashboard with Power BI

Big data is a massive asset, which businesses leverage for transforming business operations, enhancing decision making and increasing the overall productivity of the organisation. Power BI helps in analysing data effectively along with providing interactive insights for creating visually immersive visualisations and interactive dashboards. 

Building a dashboard with Power BI has become a favourite for businesses. In a Power BI dashboard, you can assemble all crucial data elements on a single page. The dashboard acts as a gateway to the underlying reports and datasets so that you can reach the reports from the dashboards whenever needed. 

By using Power BI dashboards, you can visualise your data and share insights across the organisation. You have the benefit of embedding the dashboard into your app or website. 

How to create dashboards in Power BI?

A powerful visualisation tool, Microsoft Power BI transforms raw data into actionable insights. Create custom dashboards for displaying crucial metrics and giving complete visibility to your business for informed decision-making. 

Let us look at the various steps to follow for creating dashboards in Power BI:

• Data Import 

The first step towards creating dashboards in Power BI is data import. You have to move data to Power BI first. 

• Click on the ‘Get Data’ button at the left corner bottom of the screen.
• Choose the source from where you want to import data – OneDrive, Excel, Azure SQL database or SharePoint. 
• After importing the data, Power BI generates a blank canvas with a menu where you can select visuals which represent the metrics best on the dashboard.

• Selecting the visuals

You must choose proper visuals for the reports and the data so that the important pointers are highlighted. Here are some common Power BI visualisations you can choose from:

• Bar charts – These are ideal for showcasing the comparison between various data categories. They are great for analysing monthly expenses and sales product performance. 
• Scatter plots – These represent a relationship between two attributes. 
• Pie charts – Great for representing customer demographics, pie charts represent the composition of a whole in terms of percentage. 
• Stacked charts – With these charts, you can present multiple data types within a single bar. They are useful for highlighting monthly budget breakdowns. 
• Line graphs – These are useful for tracking changes over time. They are mainly used for plotting annual revenue. 

• Attaching visuals to the dashboard

While customising the Power BI dashboard, you have to pin the most valid and suitable visual or chart. 

Click on ‘Add Title’ > ‘Create a Visual’ > Pin Icon to attach the icon to the custom dashboard. With the drag-and-drop feature, you can arrange the tiles as you feel. 

• Dashboard themes

There are options for changing the dashboard themes in Power BI so that your dashboard has a new look. With every Power BI subscription, you have some inbuilt basic themes. You also have the option of downloading custom themes from the Microsoft Themes Library. With themes, you can work on the organisation’s branding. Themes also help in differentiating the dashboards on various parameters. 

• Targeting the audience

With Microsoft Power BI, you can create effective dashboards for specific users. You can target your audience specifically for better results. For instance, if you are creating an accounting dashboard, you can highlight things like product performance, monthly expenses, revenue generation and other things. To capture the attention of your audience, Power BI lets you expand the charts and drill into the reports. Refrain from cluttering your dashboard with unnecessary information and data. 

• Sharing the dashboard

The best thing about Microsoft Power BI is that you can share the dashboard with colleagues, peers and major decision-making professionals in the organisation. You can edit the imported databases by adding or modifying content in the datasets. The visuals and charts in the Power BI update automatically with the edits. As a result, users can see the updated data, which helps in boosting the transparency and productivity of the organisation. 

Items that are customisable in the Power BI dashboards

The drag-and-drop functionality of Power BI simplifies the presentation and data extraction process in interactive data visualisations. Here are certain items which are customisable in the Power BI dashboards:

• Security filters

You have the option of setting up access filters for ensuring that viewers only see information which is relevant to them. You can overcome the risk of unauthorised access. 

• Machine Learning

Power BI users can make Machine Learning (ML) models, incorporate Azure Machine Learning, and access image recognition and text analytics. 

• Real-time data

With Power BI, you can update dashboards in real-time. This implies that data is streamed continuously letting viewers solve issues and determine opportunities on the go. Live data can go from business apps, social media or any other source. Some of the data is time-sensitive as well. 

• Cortana Integration

Cortana, the digital assistant, can be integrated into Power BI. with this integration, you can put questions in your natural language for accessing any kind of information. This is a very useful feature for users who access Power BI through their mobile devices. 

• Publication and distribution

Power BI dashboards enable direct uploading of visualisations and reports instead of uploading large files to the shared drive or sending them via email. As soon as the underlying dataset is updated with fresh data, the dashboard data is refreshed every time. 

Summing it up

Data has a superpower today. With Microsoft Power BI dashboards, you can make your data understandable to users. You can use data from various sources and develop visually immersive and impressive insights for informed decision-making in businesses. Add more value to your business with Microsoft Power BI dashboards. 

If you want to unleash the power of Power BI and understand data better, taking up a course in the subject will be of great help. Many universities and institutes offer a Data Science course with placement opportunities to interested candidates. 

Imarticus Learning offers a Post Graduate Programme in Data Science and Analytics. Build your career in Data Science and Analytics with guaranteed job assurance with this course. The course covers Python programming, logistic regression, data visualisation and related topics. The course includes a live learning module, real-world projects, a job-specific curriculum, dedicated career services and many more features. 

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Programming paradigms are different approaches that are taken to provide solutions for a variety of tasks. It consists of various rules, strategies and principles that a software developer can utilise to develop new software or enhance the efficacy of an existing one. Out of the various programming paradigms out there, Object-oriented programming and functional programming are two very important but different approaches to programming. 

All the programming languages follow at least a particular programming paradigm or are a composition of a few of them. It is upon the needs of the software and the discretion of the developer which programming paradigm is to be used. 

Read on to know about object-oriented programming and functional programming along with their differences.

What is Object-oriented Programming?

Object-oriented programming is a type of programming paradigm that works on the basis of classes and objects. Developers, in object-oriented programming, write codes that are then classified into various classes and as various objects. Developers majorly use OOP for data frame manipulation as it is well suited for the task.

Classes are blueprints or a particular set of instructions used to build a data structure for each of the objects in a specific manner. Objects are categorised based on the variables that they contain and function in the way variables behave. 

Object-oriented programming is concerned with determining the operation and performance of the variables. It can be said that classes serve as templates to build objects and the objects are instances of those particular classes. Additionally, objects also contain codes in the form of procedures and data in the form of fields. It is also known as attributes and methods.

What is Functional Programming?

Functional programming is a programming paradigm that is declarative in nature. Developers use functional programming to write codes only in pure functions. Codes in pure functions imply that functional programming does not alter old variables into new characters but only generates new variables. 

Functional programming is great for data visualisation but cannot conveniently be used for data frame manipulation. It can be said that pure functions cannot be influenced by external factors and the result is only based on the input parameters. Additionally, functional programming helps in avoiding mutable and shared data.  Continue reading “Difference between Object-oriented and functional programming”