Category: Analytics

Power Pivot is a powerful data modelling and analysis tool and can play an essential role in a Career in Data Analytics. Power Pivot is a vital tool for any data analyst or data scientist with its ability to handle large data sets and perform complex calculations.

This blog will explore how to use Power Pivot for advanced data science, including data preparation and cleaning, data modelling and analysis, and data visualisation. 

Whether you’re starting your Career in Data Analytics or are a seasoned data professional, this blog will provide valuable insights and best practices for using Power Pivot in your data science projects.

What Is Power Pivot?

Power Pivot is a Microsoft Excel add-in that provides data analysis and modelling capabilities for business intelligence and data analysis. 

A familiar Excel interface allows users to import, manipulate, and manage large amounts of data and create custom calculations, relationships, and reports. 

The Power Pivot feature enhances Excel’s capabilities, enabling users to perform advanced data analysis and reporting, including data modelling and visualisation.

Why Use Power Pivot?

Power Pivot is a powerful data analysis tool that allows you to process large amounts of data and perform complex data manipulations in minutes. It is used for advanced data analysis, particularly in business intelligence and data science. 

There are several reasons to use Power Pivot:

Ease of Use: Power Pivot has a handy interface, making it easy for non-technical users to perform complex data analysis.

Speed: Power Pivot can handle large amounts of data, allowing you to process, manipulate, and analyse data in a matter of minutes.

Integration with Excel: Power Pivot is integrated with Microsoft Excel, making it a convenient tool for those familiar with spreadsheet software.

Data Manipulation: Power Pivot enables you to perform complex data manipulations, including data cleansing, data aggregation, and data modelling, making it an ideal tool for data scientists and business analysts.

Enhanced Data Analysis: Power Pivot provides advanced data analysis features, such as pivot tables, charts, and data visualisations, which are not available in standard Excel.

Scalability: Power Pivot can scale to handle large amounts of data, making it an ideal tool for large-scale data analysis projects.

Power Pivot Use Case

A use case for Power Pivot might be for a business analyst who needs to analyse sales data from many departments and stores. 

The data is stored in separate Excel spreadsheets, and the analyst needs to combine the data and perform analysis to identify trends and make recommendations to the company.

With Power Pivot, the analyst can:

Import data from many Excel spreadsheets into a single data model.

Create relationships between the tables to link the data together.

Create calculated fields using DAX (Data Analysis Expressions) to perform custom calculations, such as finding the total sales for each department or store.

Build tables and charts to analyse the data and identify trends and patterns.

Share the data model and analysis with others by creating a Power BI report or publishing the Excel workbook to the web.

This use case demonstrates the ability of Power Pivot to handle large data sets, perform complex calculations, and provide interactive data analysis and visualisation capabilities.

How To Enable Power Pivot?

To enable Power Pivot, you need to have the Power Pivot add-in installed in your Microsoft Excel software. You can download and install it from the Microsoft website if you don’t have it.

Here are the steps to enable Power Pivot:

Open Microsoft Excel and then click on the “File” tab.

Go to the “Options” tab and select “Add-Ins.”

In the Manage box, select the option “Excel Add-ins” and then click on “Go.”

Check the “Microsoft Power Pivot for Excel” check box and click “OK.”

You should now see a Power Pivot tab in the ribbon menu.

Once you enable Power Pivot, you can import and manage large data sets, perform advanced data analysis, and create robust pivot tables and models. 

What Are the Benefits of Using a Power Pivot?

Some benefits of using Power Pivot include the following:

Handling large data sets: Power Pivot can take large amounts of data from various sources, making it easier to work with and analyse data.

Data Modeling: Power Pivot allows you to create relationships between tables, perform calculations and create calculated fields using DAX (Data Analysis Expressions).

Integration with Power BI: Power Pivot can be a data source in Power BI, a cloud-based business intelligence and data visualisation platform.

No need for VBA or Python: Power Pivot does not need any programming skills or knowledge of Python or VBA, making it accessible to a broader range of users.

Improved performance: Power Pivot uses columnar storage and in-memory technology to improve query performance, making it faster and more efficient than traditional Excel pivot tables.

Conclusion

Power Pivot is a critical tool to master for anyone aspiring to build a career in Data Science. It offers the ability to process massive data sets, execute complex calculations, and provide interactive data analysis and visualisation capabilities. 

Businesses can establish a robust data science workflow by integrating Power Pivot with other data science tools like Python and Power BI.

A data analytics certification course is a valuable investment to enhance your existing data science skills or start your journey in the field.

Imarticus’ Postgraduate Program in Data Science and Analytics is a comprehensive course that covers all aspects of data science, including data preparation, modelling, analysis, and visualisation. 

With a focus on hands-on learning and real-world projects, this data analytics certification course equips students with the necessary skills and knowledge to succeed in data science.

This data science course with placement assistance helps students with practical and job-ready education, preparing them for a successful Career in Data Science.

So take advantage of this opportunity to further your Career in Data Science. Enrol in Imarticus Learning’s Postgraduate Program in Data Science and Analytics today.

Python is a high-level and fast-growing programming language which is ideal for scripting both applications as well as websites. Even though there are several programming languages such as C++, SQL or R that are widely used by aspiring data scientists, Python stands out from the rest. 

A career in Data Analytics ensures a promising future for those who can master the fundamental programming concepts and apply them to solve real-world problems in any business.  

It is imperative to know how to employ data analytics tools to evaluate the performance of a business. Knowing a programming language like Python can be extremely effective as it helps you build these tools. 

Some data scientists, on the other hand, use R to analyse data through interactive graphics. In fact, R is a frequently chosen programming language for data visualisation. It is, however, important to understand on what grounds Python and R are different and why Python is the most preferred programming language in this profession. 

What Is Python?

Python is extremely versatile and it is among the most dynamic and adaptable programming languages used in data analysis. It is used to develop complex numeric as well as scientific applications. You can use this programming language to perform scientific calculations.  

It is an open-source and object-oriented programming language with a rich community base, libraries and an enormous arrangement of tools. Compared to other programming languages, Python, with its straightforward code, is much simpler to learn because of its broad documentation. 

What Are the Features of Python?

The significant features of this programming language are

Readable: In comparison to other programming languages, it is much easier to read. It uses less code to perform a task. 

Typed language: The variables are automatically created as it is a typed language. 

Flexible: It is quite easy to run this programming language on multiple platforms as it is flexible and adaptable. 

Open-source: It is a free programming language. It uses an easily accessible and community-based model. 

Why Is Python Important in Data Science?

Whether you are already a professional data analyst or someone who aspires to explore a lucrative career in Data Analytics, it is imperative that you know how to use Python. Some of the most prominent reasons why this programming language is preferred for data science are:

Easy to learn and use: With better comprehensibility and simple syntax it has become extremely popular over the years. It is also quite easy to handle the data through its data mining tools such as Rapid Miner, Weka, et cetera.

Builds superior analytics tools: It is a dynamic programming language that provides better knowledge and correlates data from large datasets. It also plays a crucial role in self-service analytics. 

Important for deep learning: It assists data scientists to develop deep learning algorithms which were majorly inspired by the architecture of the human brain. 

Creates data analysis scripts: Data analysis scripts can be created using this program within Power BI. 

Has a rich community base: Python developers are able to address their issues within a huge community of data scientists as well as engineers. Python Package Index, for example, is a great place for developers to explore this programming language. 

What Is R?

R is a versatile, statistical and advanced programming language which is primarily used for interpreting data. It works perfectly for data visualisation, web applications and data wrangling. It is also used to perform statistical calculations and that too without vectors. R makes collecting and analysing large datasets easy. 

What Are the Features of R?

The important features of R include

Open-source: R too, is free, adaptable and accessible to all. It can be easily integrated with multiple applications. 

Static graphics: R has powerful and interactive static graphics which produce high-quality data visualisations. 

Statistical calculations: This programming language can perform both simple as well as complex statistical calculations. 

Compatibility: This programming language is compatible with other programs such as C, C++, Java, et cetera. 

Python vs R: Which Programming Language Is Preferred in Data Science?

The primary reasons why Python is often preferred over R are:

Purpose: Both these programming languages serve different purposes. However, even though both are used by data analysts, it is Python which is considered more versatile in comparison to R. 

Users: The software developers prefer Python over R as it builds complex applications. Statisticians and researchers in academia, on the other hand, prefer using R. 

Ease of use: Beginner programmers prefer Python because of its English-like syntax. R, on the other hand, can be difficult once a programmer starts exploring its advanced functionalities. 

Popularity: Python outranks R mainly because it can be used in several software domains. 

However, despite these differences, both these programming languages have robust ecosystems of libraries and are extremely crucial for an aspirant who wishes to start a prospective career in Data Analytics.

Conclusion

A career in data science is considered one of the most successful professions in recent years. If you want to learn data analytics techniques, it is imperative that you learn Python. 

In order to pursue a career in Data Science, you should choose a proper data analytics certification course that introduces you to this programming language. 

Imarticus’ Postgraduate Program in Data Science and Analytics is a job-assurance program that helps you navigate all aspects of this profession. The curriculum of this course covers all the fundamental data analytics concepts including data analysis, introduction to important programming languages such as SQL and Python, data visualisation with Power BI or Tableau and applications of machine learning. 

Deep learning is a rapidly growing topic within artificial intelligence (AI). It focuses on developing algorithms that allow computers to learn from data. We can use Deep learning for various tasks, including image recognition, language translation, and autonomous navigation. As a result, deep learning professionals are becoming increasingly important as the need for AI grows.

In this blog post, we’ll look at some of the key deep learning methods AI practitioners need to apply to be successful. Let’s first understand,

What Is Deep Learning?

Deep learning is a powerful subset of artificial intelligence (AI) that enables computers to learn complicated tasks and processes. It is the basis for several cutting-edge AI applications, including facial recognition and self-driving automobiles.

As AI practitioners continue to explore the depths of machine learning and develop a deeper understanding of its capabilities. It is essential to understand the different methods and techniques used in deep learning. Now let’s look at the key deep learning methods AI practitioners need to be familiar with:

1. Neural Networks and Backpropagation.

One of the foundational elements of deep learning is neural networks. Neural networks are essentially a system of interconnected nodes or “neurons”. They work together to process input data to make decisions or predictions. This type of network is modelled after biological neurons found in the human brain. This network has been used in various forms since the 1940s.

For neural networks to learn, it is essential to train using backpropagation. It is a process that propagates errors backwards through the network to adjust weights and biases accordingly. This process allows neural networks to “learn” from their mistakes over time.

2. Convolutional Neural Networks.

Deep learning algorithms known as convolutional neural networks (CNNs) were created expressly for image identification applications. CNNs use convolution layers with filters that recognise image patterns more efficiently compared to traditional neural networks.

These layers are combined with pooling layers that reduce the size of images while still preserving essential features. The combination of these two layers makes it possible for CNNs to identify objects within an image with high accuracy rates accurately.

3. Recurrent Neural Networks.

Recurrent neural networks (RNNs) are another type of deep learning algorithm. They are often used when dealing with sequential data such as text or audio recordings. Unlike traditional neural networks, RNNs have feedback loops that allow them to remember information from previous inputs. That accurately predicts future outputs based on current inputs and past observations.

This makes RNNs ideal for language translation and speech recognition tasks. They are useful where understanding context is essential for accurate results.

4. Autoencoders.

Autoencoders are a type of artificial neural network used for unsupervised learning. Autoencoders reduce the size of data by representing it in a smaller format while retaining the information. This is especially useful for image recognition, where it can compress data without losing essential features.

Autoencoders are also well-suited for anomaly detection. They can identify data points significantly different from the rest of the dataset.

5. Generative Adversarial Networks.

Generative adversarial networks (GANs) are a kind of deep learning algorithm used for unsupervised learning. Unlike traditional machine learning algorithms, GANs use two networks—a generator and a discriminator. These networks interact against each other to improve the performance of both networks.

The generator produces data that it attempts to trick the discriminator into believing is real. On the other hand, the discriminator works to identify which data is real and which is generated. This competition helps both networks become more accurate over time, improving performance on various tasks.

6. Transfer Learning.

Transfer learning is a deep learning technique that leverages existing models to train new models quickly. This is especially useful for tasks with limited data, as it allows the model to learn from processed and labelled data.

Transfer learning can also be used for various tasks, such as image recognition and natural language processing. This is because it allows the model to take advantage of existing networks that have already been trained on similar tasks.

7. Long Short-Term Memory (LSTM) Networks.

Long short-term memory (LSTM) networks are a type of recurrent neural network designed to remember long-term dependencies in sequential data. Unlike traditional recurrent neural networks, LSTMs have feedback loops that allow them to store and access information from previous inputs.

This makes LSTMs ideal for language translation and text generation tasks. It is useful in situations when knowing context is critical for correct results.

Conclusion

Deep learning is an ever-evolving field within artificial intelligence (AI). There are many different methods AI practitioners need to master to stay competitive in this space. Understanding how these methods work will help practitioners stand out when applying for AI jobs. Joining the Data Analytics course with placement can help develop new projects related to deep learning technology today!