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[probabs]

Data Analytics in Accounting and Finance

In the fields of accounting and finance, the demand for data-driven decision-making has never been higher, driven by the exponential growth in both structured (e.g., databases, spreadsheets) and unstructured (e.g., social media posts, emails) data. To succeed in this era, professionals must adopt a data analyst mindset and leverage data analytics for making well-informed decisions.

[radixanalytics]

THE FUTURE OF DATA ANALYTICS

INTRODUCTION

In the ever-evolving landscape of technology, data analytics stands as a pivotal force, driving informed decision-making across industries. As we embrace the era of big data, artificial intelligence, and advanced computing, the future of data analytics promises to be both transformative and revolutionary. 

Let's embark on a journey into the realms of tomorrow's data analytics, exploring the trends, technologies, and possibilities that will shape the way we derive insights from data.

AI-POWERED ANALYTICS

The integration of artificial intelligence (AI) into data analytics is set to redefine the capabilities of predictive modeling and data interpretation.

Machine learning algorithms will play a crucial role in automating data analysis, uncovering patterns, and providing real-time insights.

AI-driven analytics will enable businesses to make faster, more accurate decisions based on a deeper understanding of their data.

EDGE ANALYTICS    

The future of data analytics will witness a shift towards decentralized processing with the rise of edge analytics.

Edge computing allows data analysis to occur closer to the source, reducing latency and enabling real-time decision-making in scenarios such as IoT devices and smart sensors.

This trend will be particularly impactful in industries where instantaneous insights are critical, such as healthcare and manufacturing.

EXPONENTIAL GROWTH OF UNSTRUCTURED DATA

With the proliferation of multimedia, social media, and other unstructured data sources, the future of analytics will grapple with managing and extracting meaningful insights from vast and diverse datasets

Natural Language Processing (NLP) and advanced text analytics will become integral to deciphering the value hidden within unstructured data, providing a more comprehensive understanding of customer sentiments and market trends.

ETHICAL AND RESPONSIBLE DATA ANALYTICS

With increased public awareness about data privacy and ethics, the future of data analytics will prioritize responsible practices.

Ethical considerations in data collection, usage, and storage will become integral, requiring organizations to establish transparent and accountable data analytics frameworks.

AUGMENTED ANALYTICS

The rise of augmented analytics will empower business users with tools that automate data preparation, insight discovery, and sharing, reducing their reliance on data scientists.

Natural language interfaces and automated insights will make data analytics more accessible to a broader audience within organizations.

CONCLUSION

The future of data analytics is an exciting frontier where technological advancements and evolving trends promise to unlock unprecedented possibilities. 

As businesses and industries adapt to these changes, the journey towards data-driven decision-making will become more dynamic, intelligent, and ethical. 

By staying at the forefront of these developments, organizations can harness the power of data analytics to navigate the complexities of the future and gain a competitive edge in an increasingly data-driven world.

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Spoiler alert: It's not that simple.

Everyone's Python journey is unique—some take months, others take years.

If I were starting today, here's what I'd do:

Prioritize understanding Python fundamentals over trendy libraries

Focus on writing clean, efficient, and readable code

Build practical projects that showcase your problem-solving skills

Hone your debugging skills to tackle complex issues

Top Python Books:

"Python Crash Course" by Eric Matthes

"Automate the Boring Stuff with Python" by Al Sweigart

"Python for Data Analysis" by Wes McKinney

"Learning Python" by Mark Lutz

"Python Cookbook" by David Beazley and Brian Kernighan

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🚀 Data-driven change-makers, you are the architects of the digital era! The world's treasure trove of data needs your insights and skills - and companies like Google are seeking your expertise. 🌐

Just as Peter Sondergaard said, 'Information is the oil of the 21st century, and is the combustion engine.' The role of a Data Analyst has never been more crucial Transform jumbled numbers into stories, forge your path in uncharted data landscapes, and unlock opportunities only understood by the language of data! 📊

Why Google? Promising a vibrant work culture that fosters innovation and growth, Google provides an ideal environment to catapult your Data Analyst career. An opportunity here't just a job - it's a ticket to refining the world, one data point at a time.

Data lovers, it's time to step up and thrive! Unlock your potential with Google's limitless resources and booming data industry expected to worth $229.4 billion by 2025. Be a part of the journey that reshapes the future. Are you ready to join data revolution 🔄🌍

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We must all remember that statistics are not just numbers but are also the key ingredients in the formulation of right plans and policies of a country.

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[brillica-design]

What is DAX Query and How is it Helpful?

DAX refers to Data Analysis Expressions i.e. such expressions or formulas which are used for data analysis and calculations. These expressions are the collection and combination of functions, operators, and constants which are evaluated as one formula to extract the results (value or values). DAX formulas are very useful in Business Intelligence tools like Microsoft Power BI as they help the data analysts to use the data sets they have to the fullest potential.

With the help of the DAX language, analysts can discover different new ways of calculating data values they have and come up with the new outcomes.

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Know more about DAX concepts:

• DAX is a functional language i.e., its absolute code is always a function. A feasible DAX expression contains conditional statements, nested functions, value references, etc.

• DAX formulae have two primary data types; Numeric and Non-numeric or Others. The numeric data type includes integers, decimals, currency, etc. Whereas, the non-numeric comprises of strings and binary objects.

• DAX expressions are classified from the innermost function going to the outermost one at the last. This makes composing of a DAX formula important.

• You can use values of mixed data types as inputs in a DAX formula and the conversion will take place automatically during execution of the formula. The output values will be converted into the data type you have given for the DAX formula.

Why DAX is Important?

Making reports using the functionalities of the data importation, transformation and visualisation is a smooth experience. A user needs to have basic knowledge of the tools to create a decent report with all the available data. But, if you want to achieve a level up and use advanced calculations in your reports, you need to have the knowledge of DAX.

For instance you want to make a visualisation report to analyse the percentage of growth over the different states of a country or you want to compare year-after-year growth or sales. The data fields which you have imported in a data table are generally not enough for the goal you want to achieve.

For these needs, you need to build new measures using DAX language. In this way, you can create new measures, use them for creating exclusive visualizations, and have unique perceptions into the data. With such unique perceptions into the data, you can have fixing solutions for the business problems that you might miss with the usual way of analysis. Thus, DAX makes data analysis a smart and an intelligent approach.

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DAX Formula – Syntax

The initial and the most crucial step in learning any language is to break it down into conclusive elements and understanding the elements. And, this is why we study the syntax of a language. Given below is an example of the DAX formula.

                                   Types of Calculations in DAX

Evidently, the DAX formulas can also be called as calculations as they calculate an given value and return the desired value. We can create two types of expressions or calculations using DAX ; calculated columns and calculated measures.

Calculated Columns: The calculated columns create a new column in the existing table. The only difference between a regular column and a calculated column is that it is required to have at least one function in the calculated column. This calculated column is used when you want to create a column with filtered or sorted information.

Calculated Measures: A calculated measure creates a field having aggregated values such as sum, ratios, percentages, averages, etc.

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Functions in DAX

A DAX function is a pre-determined formula which performs the calculations on the values provided to it in arguments. The arguments during a function got to be during a particular order and may be a column reference, numbers, text, constants, another formula or function, or a logical value such as TRUE or FALSE. Every function performs a particular operation on the values encompassed in an argument. You can use quite one argument during a DAX formula.

Context of DAX

The concept of DAX context is crucial in gaining a complete understanding of DAX language and how it works. There are two sorts of DAX contexts; Row context and Filter context.

Row context: means taking under consideration a selected row that has been filtered during a DAX expression. The row context focuses on the operation of the formula on the present row. This type of context is most frequently applied to the measure rows.

Filter context: focuses on values one step ahead. In row context, we use filtering out and applying operations on specific rows. But in filter context, the expression applies a filter to specialise in specific values within a row. Thus, the filter context is applied in addition to the row context to narrow down the scope of calculation to specific values. Filter context is applied once we use functions like CALCULATE, FILTER, RELATED, ALL, etc.

We hope this blog have helped you all enough with basic knowledge of DAX formulas. You can create any logically sound DAX formula by using the existing columns and tables in your tool to make more effective reports.

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[haris1828]

Data Science Modeling

The process of outlining the connections between various types of information that will be kept in a database is known as data modeling. One goal of data modeling is to determine the most efficient way to store data while yet allowing for full access and reporting.

What is Data Science?

In order to extract significant insights from data, the field of study known as "data science" combines subject-matter expertise, programming prowess, and proficiency in math and statistics.

Data scientists develop artificial intelligence (AI) systems that can perform tasks that frequently need human intellect by employing machine learning algorithms on a variety of data types, including numbers, text, pictures, videos, and audio. Analysts and business users can then translate the insights these technologies produce into actual economic value.

Why Data Science is important?

Data science, artificial intelligence, and machine learning are becoming more and more important to businesses. Businesses of any size or sector must act swiftly to develop and implement data science capabilities if they are to remain competitive in the big data era.

Key Skills required in Data Science

According to data science firms, the ideal person must have a particular set of skills before beginning data science modeling. To execute data science modeling, the following skills are necessary:

Probability and Statistics

Programming abilities

Skills in Data Visualization

Machine Learning and Deep Learning

Communication Skills 

1) Probability and statistics

Probability and statistics provide the basis of data science. Making forecasts benefits from understanding probability theory. Estimations and projections are essential in data science. Statistical techniques are used by data scientists to estimate the outcomes of upcoming study. The application of probability theory in statistical procedures is very widespread. The basis of all statistics and probability is data.

2) Programming abilities

Python is the most popular programming language used in data science, but other languages including R, Perl, C/C++, SQL, and Java are also utilised. Data scientists can use these programming languages to organise collections of unstructured data.

3) Skills in Data Visualization

Sketches are primarily read, whereas the most important newspaper stories are only skimmed and disregarded. Humans believe that when they see something, it is registered in their minds. The entire dataset, which could have hundreds of pages, can be turned into two or three graphs or plots. You must first view the Data Patterns in order to create a graph.

4) Machine Learning and Deep Learning

Machine learning expertise is a requirement for any data scientist. Predictive models are created using machine learning. For instance, if you want to forecast how many clients you'll have in the following month based on the data from the previous month, you'll need to employ Machine Learning techniques. Machine learning and deep learning algorithms are the basis of data science modeling.

5) Communication Skills

Senior Management or a group of Team Members must hear your results. By employing communication, we can get beyond the issues that everyone is fighting for. You can convey ideas more clearly and identify inconsistencies in data if you have good communication skills. Presentation skills are crucial for displaying Data Discoveries and creating future strategies in a project.

Procedures for Data Science Modeling

The following are the main steps in data science modeling:

Step 1: Understanding the Problem

Step 2: Data Extraction 

Step 3: Data Cleaning

Step 4: Exploratory Data Analysis

Step 5: Feature Selection

Step 6: Incorporating Machine Learning Algorithms

Step 7: Testing the Models 

Step 8: Deploying the Model

Step 1: Understanding the Problem

The first stage in the Data Science Modeling process is to understand the problem. A data scientist listens for keywords and phrases when chatting with a line-of-business specialist about a business scenario. The Data Scientist deconstructs the problem into a procedural flow that always includes a thorough understanding of the business challenge, the Data that must be collected, and the Artificial Intelligence and Data Science approaches that can be used to solve the problem.

Step 2: Data Extraction 

The next stage of data science modeling is data extraction. The bits of unstructured data you collect that are relevant to the business problem you're trying to solve, not just any data. Data is gathered from a number of different websites, surveys, and pre-existing datasets.

Step 3: Data Cleaning

Since you must sanitise data as you collect it, data cleaning is beneficial. The following list includes some of the most typical causes of data discrepancies and errors:

Duplicate items are eliminated from various databases.

Input with precision-related inaccuracy data

Changes, updates, and deletions are made to the Data entries.

Variables in several databases lack values.

Step 4: Exploratory Data Analysis

A trusted technique for getting comfortable with data and extracting insightful information is exploratory data analysis (EDA). Data scientists sift through unstructured data to look for patterns and infer relationships between different data points. Data scientists use statistics and visualisation tools to summarise Central Measurements and variability for EDA.

Step 5: Feature Selection

Identifying and selecting the attributes that have the greatest impact on the output or forecast variable that interests you can be done manually or automatically.

Your model may become less accurate and train using irrelevant features if your data contains irrelevant characteristics. In other words, if the traits are strong enough, the machine learning algorithm will provide outstanding outcomes.

Step 6: Incorporating Machine Learning Algorithms

One of the most crucial tasks in data science modeling is the creation of a functional data model, which the machine learning algorithm aids in doing. There are numerous algorithms available, and the model selected depends on the problem.

Step 7: Testing the Models 

This is the stage where we must ensure that our Data Science Modeling efforts are up to par. The Data Model is used to the Test Data in order to determine its accuracy and the presence of all desired characteristics. To detect any adjustments that might be required to boost performance and achieve the desired results, you can run additional tests on your data model. In the event that the required precision is not attained, you can go back to Step 5 (Machine Learning Algorithms), choose a different data model, and test the model once more.

Step 8: Deploying the Model

The model that provides the best output is finalised and deployed in the production environment once the desired outcome has been achieved through suitable testing in accordance with business goals.

Conclusion

The steps for performing data science modeling are covered in this post. Integrating data from diverse sources is the initial step in putting any Data Science algorithm into effect.

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