#Power Bi Data Visualisation

[amartam]

Power BI Data Visualisation | Data Visualisation Tools

Power BI is a business analytics service by Microsoft that provides tools for aggregating, analysing, visualizing, and sharing data. Data visualization in Power BI is a key feature that allows users to create interactive and visually appealing reports and dashboards. Unleash the full potential of your data with our expert Power BI Data Visualization services. At Amartam Technology Solutions Pvt. Ltd, we turn complex datasets into clear, actionable insights, empowering you to make informed decisions with confidence. Our team of Power BI Data Visualisation specialists is here to guide you every step of the way, from setup to advanced analytics. Experience real-time, interactive dashboards that bring your data to life and make analysis effortless. Turn your data into a powerful asset and drive your business forward with clarity and precision.

[zora28]

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[powerbisolutions-blog]

Enhancing Project Management with Power BI Reporting and Dashboards 

Efficient project management is the cornerstone of successful businesses, and having the right tools can make all the difference. Power BI, a leading business analytics platform, is revolutionizing project management by offering robust reporting and dashboard capabilities that provide real-time insights and data-driven decision-making. 

Transforming Project Management with Power BI 

Power BI empowers project managers by consolidating data from multiple sources into interactive dashboards. These dashboards enable teams to monitor project progress, track key performance indicators (KPIs), and identify potential bottlenecks—all in real time. With customizable reports, managers can tailor insights to align with specific goals, ensuring that every stakeholder has the information they need at their fingertips. 

For example, Power BI’s real-time monitoring allows project managers to oversee task completion rates, budget utilization, and resource allocation. Alerts and automated notifications can be set up to identify risks early, ensuring timely interventions. By offering a centralized view of project data, Power BI eliminates the need for time-consuming manual updates, freeing teams to focus on what matters most—delivering successful projects. 

Seamless Integration and Scalability 

One of Power BI’s standout features is its ability to integrate seamlessly with tools like Microsoft Excel, Azure, and even ERP systems like Acumatica. This means project teams can work within a familiar ecosystem while leveraging the advanced analytics and visualization capabilities of Power BI. Whether your business is a small startup or a large enterprise, Power BI’s scalable solutions grow with you, ensuring consistent performance and reliability. 

Empowering Teams with Exceptional Support 

At Power BI Solutions, we specialize in delivering tailored dashboards and reports designed to meet the unique needs of your business. Our certified professionals provide end-to-end implementation and ongoing training, ensuring your team is equipped to unlock the full potential of Power BI. From automating reporting processes to creating actionable insights, we are dedicated to empowering businesses with data-driven project management solutions. 

Conclusion 

With its interactive dashboards, real-time reporting, and seamless integration, Power BI is transforming project management. By simplifying data analysis and enhancing decision-making, Power BI equips businesses to achieve their project goals efficiently and effectively. 

[infromationhub]

Insights in Focus: Empower Your Data with Professional Data Visualization Consulting Services

Data is a valuable asset for businesses, but its true power lies in the ability to derive meaningful insights from it. That's where data visualization consulting services come into play. In this digital age, businesses have access to an abundance of data, but understanding and effectively communicating that data can be a challenge. That's why partnering with professional data visualization consulting services can empower your business to unlock the full potential of your data, gaining valuable insights and making informed decisions.

Harnessing the expertise of data visualization consulting services allows you to transform complex data sets into visually compelling and easy-to-understand representations. This process involves using a combination of design principles, statistical techniques, and storytelling to create visually engaging dashboards, infographics, and interactive visualizations that bring your data to life.

Data visualization is not just about creating aesthetically pleasing charts and graphs; it's about telling a story with data. Skilled consultants in data visualization understand the art and science of presenting data in a way that highlights key trends, patterns, and relationships. They take into account the target audience and tailor the visualizations to suit their needs, ensuring that the message is clear and impactful.

One of the primary benefits of data visualization consulting services is the ability to gain actionable insights from complex data sets quickly. Visual representations of data enable you to spot trends, outliers, and correlations that may have gone unnoticed in raw data. With interactive visualizations, you can drill down into specific data points, explore different scenarios, and uncover valuable insights that drive informed decision-making.

Moreover, effective data visualization helps you communicate your findings and insights to stakeholders, executives, and team members in a compelling and easily understandable manner. Whether it's presenting sales performance, customer behavior, or operational metrics, data visualization consulting services help you present data-driven narratives that resonate with your audience. This enhances communication, facilitates collaboration, and enables everyone to make data-informed decisions.

Data visualization consulting services also keep up with the latest trends and technologies in the field. They have expertise in utilizing advanced tools and software for data visualization, ensuring that your visualizations are not only visually appealing but also interactive, responsive, and accessible across different devices and platforms.

By leveraging professional data visualization with power bi consulting services, you can unlock the true value of your data, gain insights that drive business growth, and effectively communicate complex information. Whether you're looking to enhance your data reporting capabilities, improve decision-making processes, or enhance customer experiences, data visualization consulting services can provide the expertise and guidance you need.

In conclusion, data visualization consulting services offer a strategic approach to transform your raw data into compelling visual narratives. By partnering with experts in data visualization, you can empower your business to make data-informed decisions, communicate insights effectively, and gain a competitive edge in the modern age. Don't let your data remain hidden and underutilized. Embrace the power of data visualization consulting services and unlock the true potential of your data-driven insights.

[kikusharda123]

Learn to Clean, Visualise, and Analyse Real-World Data

In today's digital-first world, learning to work with real-world data is not just a bonus skill—it's a must. Every industry relies heavily on data-driven decision-making, from healthcare to retail, education to finance. But raw data is rarely perfect. To unlock its value, one must know how to clean, visualise, and analyse it effectively.

Even if you're a beginner or an experienced analyst, these tips and tricks will help you reach the level you desire. When going for a data science certification in Pune, it is recommended that one’s training focuses on the following three areas.

Let me explain each step in detail with real-life examples to make the work easier to understand.

Why Cleaning Data Matters

Also, it is vital and accurate to note that raw data is often filled with various errors, such as missing values and inconsistencies in the results. A lack of cleaning may cause incorrect conclusions when analyzing data, as it will contain irrelevant information.

Real-Life Example:

For your purpose, assume that you have been provided with customer feedback data of an e-commerce platform. It is dangerous to make decisions based on such data because 20% of the email addresses are incorrectly formatted, and 15% of the fields related to feedback data are empty.

Key Steps in Data Cleaning:

Assume safely that there are no redundant values stored in the form; therefore, it is recommended to check and eliminate such entries.

Dealing with missing values can be addressed in different ways, depending on the circumstances that led to the missing data. This can be done by filling in the missing values with the mean, median, or by ignoring them.

Review for errors: These include spelling mistakes, syntax, and the use of incorrect formats, especially in dates.

Check for Coherence: Inability to maintain overall homogeneity, including consistency of units of measurement.

As a learner in the data science training in Pune, you would be able to handle datasets in the training mode, which will enable you to work on live datasets that you are likely to encounter in your working environment.

Visualizing the overwhelming volume of information we come across on the internet can often appear daunting. The task of visualising the vast amount of information one encounters on the internet. The process of making the loads of information more comprehensible.

The second step that follows data cleaning is the visualisation stage. Data presentation for Game Reporting Executives transforms data into a narrative — a method of making information easy to understand and use.

Real-Life Example:

One of the latest up-and-coming companies from Pune did the same for tracking customer acquisition costs. Thus, when presented with the data in the form of bar charts and heat maps, they were able to discover that Instagram campaigns yield a 40% better return on investment than email marketing.

Popular Visualisation Tools and Techniques:

Charts and Graphs: Bar charts, pie charts, line graphs, and scatter plots help spot patterns easily.

Dashboards: Create interactive dashboards to monitor KPIs in real-time.

Heatmaps: Useful for understanding intensity across variables, such as customer activity on a website.

Infographics: Simplify large datasets into visually appealing formats for presentations or reports.

Joining a data science course in Pune often gives you access to advanced tools like Tableau, Power BI, and Python libraries such as Matplotlib and Seaborn, helping you master the art of visual storytelling.

Analysing Data: Finding Insights and Driving Decisions

Data analysis is where the magic happens. After cleaning and visualising, the next goal is to extract meaningful insights that can influence real-world decisions.

Real-Life Example:A hospital chain used historical patient data to predict peak admission times. Through regression analysis and time-series forecasting, they optimised staff allocation, reducing patient wait times by 25%.

Standard Data Analysis Techniques:

Descriptive Analysis: Summarising data to understand “what happened.”

Diagnostic Analysis: Digging deeper to find out “why it happened.”

Predictive Analysis: Using statistical models to forecast “what might happen.”

Prescriptive Analysis: Suggesting actions based on predictive outcomes.

As part of a data science certification in Pune, you will engage in projects where you analyse real-world datasets — from predicting sales to identifying customer churn.

Integrating Cleaning, Visualisation, and Analysis: The Complete Process

While each of these steps can be powerful individually, their true strength lies in their integration. A successful data project typically follows a workflow like:

Import and Inspect Data

Clean and Prepare Data

Visualise Trends and Patterns

Analyse and Model Data

Communicate Insights to Stakeholders

If you're pursuing data science training in Pune, you'll likely work on capstone projects where you complete the entire pipeline, simulating real-world workplace challenges.

Tips to Excel at Handling Real-World Data

Practice Regularly: Work on diverse datasets from different industries.

Stay Updated: Learn the latest data cleaning, visualisation, and analysis tools.

Collaborate: Participate in data science communities and hackathons.

Document Your Work: Always keep track of the cleaning and transformation steps for reproducibility.

Many professionals in Pune choose to enhance their skills through a structured data science course in Pune that emphasises practical learning over theory.

Conclusion

Learning to clean, visualize, and analyze real-world data is a transformative skill that can open up numerous career paths. Whether you aim to become a data analyst, data scientist, or business intelligence expert, mastering these three steps is non-negotiable.

Selecting a data science certification in Pune can be the perfect launchpad to build these capabilities. With abundant real-world projects, expert mentorship, and a vibrant tech ecosystem, Pune offers everything an aspiring data professional could wish for.

So, if you're ready to work with real-world messy data, tell compelling data stories through visualisations, and drive powerful decisions through analysis, now is the time to begin your journey. Find the right data science course in Pune and start building the skills that the future demands.

[yasirinsights]

Data Engineer vs Data Analyst vs Data Scientist vs ML Engineer: Choose Your Perfect Data Career!

In today’s rapidly evolving tech world, career opportunities in data-related fields are expanding like never before. However, with multiple roles like Data Engineer vs Data Analyst vs Data Scientist vs ML Engineer, newcomers — and even seasoned professionals — often find it confusing to understand how these roles differ.

At Yasir Insights, we think that having clarity makes professional selections more intelligent. We’ll go over the particular duties, necessary abilities, and important differences between these well-liked Data Engineer vs Data Analyst vs Data Scientist vs ML Engineer data positions in this blog.

Also Read: Data Engineer vs Data Analyst vs Data Scientist vs ML Engineer

Introduction to Data Engineer vs Data Analyst vs Data Scientist vs ML Engineer

The Data Science and Machine Learning Development Lifecycle (MLDLC) includes stages like planning, data gathering, preprocessing, exploratory analysis, modelling, deployment, and optimisation. In order to effectively manage these intricate phases, the burden is distributed among specialised positions, each of which plays a vital part in the project’s success.

Data Engineer

Who is a Data Engineer?

The basis of the data ecosystem is built by data engineers. They concentrate on collecting, sanitising, and getting data ready for modelling or further analysis. Think of them as mining precious raw materials — in this case, data — from complex and diverse sources.

Key Responsibilities:

Collect and extract data from different sources (APIS, databases, web scraping).

Design and maintain scalable data pipelines.

Clean, transform, and store data in warehouses or lakes.

Optimise database performance and security.

Required Skills:

Strong knowledge of Data Structures and Algorithms.

Expertise in Database Management Systems (DBMS).

Familiarity with Big Data tools (like Hadoop, Spark).

Hands-on experience with cloud platforms (AWS, Azure, GCP).

Proficiency in building and managing ETL (Extract, Transform, Load) pipelines.

Data Analyst

Who is a Data Analyst?

Data analysts take over once the data has been cleansed and arranged. Their primary responsibility is to evaluate data in order to get valuable business insights. They provide answers to important concerns regarding the past and its causes.

Key Responsibilities:

Perform Exploratory Data Analysis (EDA).

Create visualisations and dashboards to represent insights.

Identify patterns, trends, and correlations in datasets.

Provide reports to support data-driven decision-making.

Required Skills:

Strong Statistical knowledge.

Proficiency in programming languages like Python or R.

Expertise in Data Visualisation tools (Tableau, Power BI, matplotlib).

Excellent communication skills to present findings clearly.

Experience working with SQL databases.

Data Scientist

Who is a Data Scientist?

Data Scientists build upon the work of Data Analysts by developing predictive models and machine learning algorithms. While analysts focus on the “what” and “why,��� Data Scientists focus on the “what’s next.”

Key Responsibilities:

Design and implement Machine Learning models.

Perform hypothesis testing, A/B testing, and predictive analytics.

Derive strategic insights for product improvements and new innovations.

Communicate technical findings to stakeholders.

Required Skills:

Mastery of Statistics and Probability.

Strong programming skills (Python, R, SQL).

Deep understanding of Machine Learning algorithms.

Ability to handle large datasets using Big Data technologies.

Critical thinking and problem-solving abilities.

Machine Learning Engineer

Who is a Machine Learning Engineer?

Machine Learning Engineers (MLES) take the models developed by Data Scientists and make them production-ready. They ensure models are deployed, scalable, monitored, and maintained effectively in real-world systems.

Key Responsibilities:

Deploy machine learning models into production environments.

Optimise and scale ML models for performance and efficiency.

Continuously monitor and retrain models based on real-time data.

Collaborate with software engineers and data scientists for integration.

Required Skills:

Strong foundations in Linear Algebra, Calculus, and Probability.

Mastery of Machine Learning frameworks (TensorFlow, PyTorch, Scikit-learn).

Proficiency in programming languages (Python, Java, Scala).

Knowledge of Distributed Systems and Software Engineering principles.

Familiarity with MLOps tools for automation and monitoring.

Summary: Data Engineer vs Data Analyst vs Data Scientist vs ML Engineer

Data Engineer

Focus Area: Data Collection & Processing

Key Skills: DBMS, Big Data, Cloud Computing

Objective: Build and maintain data infrastructure

Data Analyst

Focus Area: Data Interpretation & Reporting

Key Skills: Statistics, Python/R, Visualisation Tools

Objective: Analyse data and extract insights

Data Scientist

Focus Area: Predictive Modelling

Key Skills: Machine Learning, Statistics, Data Analysis

Objective: Build predictive models and strategies

Machine Learning Engineer

Focus Area: Model Deployment & Optimisation

Key Skills: ML Frameworks, Software Engineering

Objective: Deploy and optimise ML models in production

Frequently Asked Questions (FAQS)

Q1: Can a Data Engineer become a Data Scientist?

Yes! With additional skills in machine learning, statistics, and model building, a Data Engineer can transition into a Data Scientist role.

Q2: Is coding necessary for Data Analysts?

While deep coding isn’t mandatory, familiarity with SQL, Python, or R greatly enhances a Data Analyst’s effectiveness.

Q3: What is the difference between a Data Scientist and an ML Engineer?

Data Scientists focus more on model development and experimentation, while ML Engineers focus on deploying and scaling those models.

Q4: Which role is the best for beginners?

If you love problem-solving and analysis, start as a Data Analyst. If you enjoy coding and systems, a Data Engineer might be your path.

Published By:

Mirza Yasir Abdullah Baig

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Gemini Looker: AI-Driven Insights & Streamlined Development

Conversational, visual, AI-powered data discovery and integration are in Gemini Looker.

Google's trusted semantic model underpins accurate, reliable insights in the AI era, and today at Google Cloud Next '25, it's announcing a major step towards making Looker the most powerful data analysis and exploration platform by adding powerful AI capabilities and a new reporting experience.

Conversational analytics using Google's latest Gemini models and natural language is now available to all platform users. Looker has a redesigned reporting experience to better data storytelling and exploration. All Gemini Looker clients can use both technologies.

Modern organisations require AI to find patterns, predict trends, and inspire intelligent action. Looker reports and Gemini make business intelligence easier and more accessible. This frees analysts to focus on more impactful work, empowers enterprise users, and reduces data team labour.

Looker's semantic layer ensures everyone uses a single truth source. Gemini Looker and Google's AI now automate analysis and give intelligent insights, speeding up data-driven organisational decisions.

All Looker users can now utilise Gemini

To make sophisticated AI-powered business intelligence (BI) accessible, you introduced Gemini in Looker at Google Cloud Next ’24. This collection of assistants lets customers ask their data queries in plain language and accelerates data modelling, chart and presentation building, and more.

Since then, those capabilities have been in preview, and now that the product is more accurate and mature, they should be available to all platform users. Conversational Analytics leverages natural language queries to deliver data insights, while Visualisation Assistant makes it easy to configure charts and visualisations for dashboards using natural language.

Formula Assistant provides powerful on-the-fly calculated fields and instant ad hoc analysis; Automated Slide Generation creates insightful and instantaneous text summaries of your data to create impactful presentations; and LookML Code Assistant simplifies code creation by suggesting dimensions, groups, measures, and more.

Business users may perform complex procedures and get projections using the Code Interpreter for Conversational insights in preview.

Chat Analytics API

We also released the Conversational Analytics API to expand conversational analytics beyond Gemini Looker. Developers may now immediately add natural language query capabilities into bespoke apps, internal tools, or workflows due to scalable, reliable data modelling that can adapt to changing requirements and secure data access.

This API enables you develop unique BI agent experiences using Google's advanced AI models (NL2SQL, RAG, and VizGen) and Looker's semantic model for accuracy. Developers may easily leverage this functionality in Gemini Looker to build user-friendly data experiences, ease complex natural language analysis, and share insights from these talks.

Introduce Looker reports

Self-service analysis empowers line-of-business users and fosters teamwork. Looker reports integrate Looker Studio's powerful visualisation and reporting tools into the main Looker platform, boosting its appeal and use.

Looker reports, which feature native Looker content, direct linkages to Google Sheets and Microsoft Excel data, first-party connectors, and ad hoc access to various data sources, increase data storytelling, discovery, and connectivity.

Interactive reports are easier than ever to make. Looker reports includes a huge collection of templates and visualisations, extensive design options, real-time collaboration features, and the popular drag-and-drop interface.

New reporting environment coexists with Looker Dashboards and Explores in Gemini Looker's regulated framework. Importantly, Gemini in Looker readily integrates with Looker Reports, allowing conversational analytics in this new reporting environment.

Continuous integration ensures faster, more dependable development

Google Cloud is automating SQL and LookML testing and validation by purchasing Spectacles.dev, enabling faster and more reliable development cycles. Strong CI/CD methods build data confidence by ensuring semantic model precision and consistency, which is critical for AI-powered BI.

Looker reports, the Conversational Analytics API, Gemini, and native Continuous Integration features promote an AI-for-BI platform. Nous make powerful AI, accurate insights, and a data-driven culture easier than ever.

Attend Google Cloud Next to see Gemini Looker and hear how complete AI for BI can convert your data into a competitive advantage. After the event, Gemini Looker offers AI for BI lecture.

[hejextechnology]

Unlock Data Insights with the Best Power BI Course in Chennai!

Want to turn data into powerful business decisions? Join Hejex Technology’s Power BI Course in Chennai, designed for beginners and professionals looking to master data visualisation and business intelligence.

Our hands-on training helps you build stunning dashboards, analyze data like a pro, and create interactive reports using Power BI. Whether you're searching for a Power BI course near me or specifically a Power BI course in Tambaram, we've got you covered!

Why Choose Hejex?

✅ Real-Time Projects & Case Studies

✅ Learn from Experienced BI Professionals

✅ 100% Practical Training

✅ Easy Access from Pallavaram & Tambaram

Build your data career with confidence and stand out in today’s data-driven world. 📍Location: Pallavaram & Tambaram, Chennai

[pink-city-to-datascience-city]

How Data Science Can Skyrocket Your Salary Growth

In modern society, information is power, and businesses will be more than willing to employ people with analytical skills to work on it. This article is good for the person who wants to change their career or just wishes to earn more money, and data science is one of the more rewarding careers.  

To answer the latter question and understand how data science can increase your income, let us then define some of the key components of data science below. And what actions have to be taken to be able to enter the sphere? In this write-up, I will provide all the information about how data science can augment your earnings if you want to take a Data Science course in Jaipur.  

Why Data Science Pays So Well  

1. High Demand and Low Supply  

With the recent surge in the popularity of data science, the industry is facing a significant shortage of professionals with the requisite skills. Sectors such as finance, healthcare, e-commerce, and IT are leading the charge in hiring data scientists, driving up the salary rates due to this scarcity.  

For instance, in a report by Glassdoor, data scientists are some of the best-paid tech workers across the world. Data Scientists, right from the fresher's, start with an average package of around 6-8 lakhs per annum, and those who have experience generally earn more than 20 lakhs per annum.  

If you are already located in Rajasthan and seeking to work on this opportunity, you need to undertake data science training in Jaipur to equip yourself with the right skills and knowledge of the field.  

 2. Salary Hikes Across Experience Levels  

In contrast to most professions, data science does not tend to stagnate pay in a few years. Below is a general guide to the wage escalations:  

Entry-Level (0-2 years): ₹6-8 LPA  

Mid-Level (3-5 years): ₹12-15 LPA  

Senior-Level (5+ years): ₹20-30 LPA  

Leadership Positions (10+ years): ₹50 LPA and beyond  

Such a growth pattern is unique to data science and is not commonly found in other domains. Uniqueness often intrigues practitioners in other fields, such as software development or marketing channels, and is pursued for the sake of monetary gains. 

If you want to fast-track your career, a structured data science course in Jaipur can provide you with hands-on training and help you land high-paying job roles faster.  

Key Skills That Increase Your Salary in Data Science  

Not all data science professionals earn the same. Your salary depends on your expertise in key areas like:  

 1. Machine Learning and AI  

Companies are investing heavily in artificial intelligence and automation. If you specialise in machine learning and deep learning, your salary potential increases significantly.  

For instance, data scientists with AI expertise earn 20-30% more than those without it. Many data science institutes in Jaipur now offer specialized training in AI and ML, helping professionals land top-tier jobs.  

 2. Big Data and Cloud Computing  

With massive amounts of data being generated every second, companies need professionals skilled in handling big data. Tools like Hadoop, Spark, and cloud platforms (AWS, Google Cloud) are highly valued in the industry.  

If you're considering data science training in Jaipur, look for courses that cover these technologies, as they can significantly boost your employability and salary prospects.  

 3. Business Intelligence and Analytics  

Data science isn't just about coding—it's also about deriving actionable insights. If you understand how to translate data into business strategies, your value in the job market increases.  

Companies prefer data scientists who can:  

Present insights in a way that decision-makers can understand  

Use visualisation tools like Tableau and Power BI  

Work on real-world business problems  

A data science course in Jaipur that includes business intelligence training can help you stand out in the job market.  

Industries Offering the Highest Salaries in Data Science  

Your salary also depends on the industry you work in. Some sectors pay significantly higher than others.  

 1. Finance and Banking  

Banks and financial institutions use data science for fraud detection, risk analysis, and investment strategies. Professionals in this field often earn between ₹12-25 LPA, even at mid-career levels.  

 2. Healthcare and Pharmaceuticals  

With AI-driven drug discovery and predictive healthcare models, data science is transforming healthcare. Salaries in this sector are highly competitive, often exceeding ₹20 LPA for experienced professionals.  

 3. E-commerce and Retail  

Companies like Amazon, Flipkart, and Myntra use data science for recommendation systems, customer behavior analysis, and demand forecasting. Salaries in this sector range from ₹8-20 LPA.  

 4. IT and Software Services  

Tech giants like Google, Microsoft, and TCS are always on the lookout for skilled data scientists. If you're already in IT and want a pay raise, a data science course in Jaipur can be a great way to transition into higher-paying roles.  

 How to Maximize Your Salary Growth in Data Science  

 1. Get Certified from a Reputable Institute  

Self-learning is excellent, but structured training accelerates your career. Many companies prefer candidates who have completed industry-recognized data science training in Jaipur.  

Before enrolling, compare data science course fees in Jaipur and choose a program that offers:  

Hands-on projects  

Placement assistance  

Certifications from recognised organisations  

 2. Build a Strong Portfolio  

Employers value practical experience. Work on real-world datasets, participate in Kaggle competitions, and showcase your projects on GitHub.  

 3. Keep Learning Advanced Tools  

Data science is an evolving field. Stay updated with:  

New machine learning algorithms  

Advanced statistical methods  

Programming languages like Python and R  

 4. Network and Apply Strategically  

Engage with the data science community on LinkedIn, attend industry conferences, and apply to companies that value data-driven decision-making.  

Final Thoughts: Is a Career in Data Science Worth It?  

If salary growth is a priority for you, data science is one of the best career choices. With high demand, lucrative pay, and diverse career paths, it offers professionals the opportunity to grow financially and professionally.  

If you're serious about entering this field, enrolling in a data science course in Jaipur can be your first step. Compare data science course fees in Jaipur and choose a program that provides intense industry exposure and hands-on training.  

With the right skills and a strategic approach, data science can help you achieve substantial salary growth and long-term career success.

[excelrsolutionshyderabad]

Performance Optimisation in Power BI: Speeding Up Reports

In today's data-driven world, Power BI has become a crucial tool for businesses to visualise and analyse their data effectively. However, as datasets increase in size and complexity, Power BI reports may experience performance slowdowns. Optimising the performance of Power BI reports is essential to ensure fast load times and seamless user experience. Let’s try to explore the key techniques to enhance Power BI performance, focusing on reducing dataset size and using aggregations. If you are pursuing a Data Analyst Course or considering a Data Analyst Course in Pune, mastering these optimisation techniques will enhance your data visualisation expertise.

Why Performance Optimisation in Power BI Matters

A well-optimised Power BI report ensures faster data refresh, quicker dashboard interaction, and a smoother user experience. Slow reports can lead to inefficiencies, delayed decision-making, and frustration among users. Performance tuning in Power BI involves reducing unnecessary data processing, optimising queries, and implementing best practices to enhance speed.

Key Techniques to Optimise Power BI Performance

1. Reduce Dataset Size

One of the core reasons for slow performance in Power BI is handling large datasets. Here are a few ways to reduce dataset size:

Remove Unnecessary Columns and Rows: Import only the data that is required for analysis. Avoid loading redundant columns or historical data that is not needed.

Use Summary Tables: Instead of importing granular-level data, use aggregated data where possible.

Filter Data at the Source: When connecting to a database, apply filters at the query level instead of filtering after data is loaded into Power BI.

Optimise Data Types: Use appropriate data types to minimise storage consumption. For example, using an integer instead of a decimal number can improve efficiency.

2. Implement Aggregations

Aggregations in Power BI improve performance by precomputing summarised data, reducing the need to process large amounts of detailed information in real time.

Use Pre-Aggregated Tables: Creating pre-aggregated summary tables ensures Power BI queries require minimal processing.

Leverage the Aggregation Feature in Power BI: Define aggregations in Power BI’s data model, enabling it to query summary data instead of raw detailed data.

Partitioning Data for Better Performance: Partitioning helps distribute data into smaller, more manageable parts, making queries run faster.

3. Optimise Data Model

A well-structured data model can significantly impact report performance. Consider these best practices:

Star Schema Over Snowflake Schema: A star schema simplifies relationships and improves query performance compared to a snowflake schema.

Reduce Cardinality: Lower the number of unique values in columns to optimise memory usage and improve aggregation speed.

Use Measures Instead of Calculated Columns: Measures are optimised for performance as they are calculated only when needed, unlike calculated columns that increase data model size.

4. Optimise Power Query Performance

Power Query is the tool used for transforming data in Power BI. Optimising queries can significantly enhance report performance.

Reduce Steps in Query Editor: Minimise the number of transformation steps to improve efficiency.

Use Query Folding: Ensure that transformations are pushed back to the source database to reduce processing time within Power BI.

Disable Auto Date/Time: By default, Power BI creates automatic date/time hierarchies, which can consume additional memory. Disabling this feature for large datasets helps optimise performance.

5. Optimise Visuals and DAX Formulas

Overloaded visuals and inefficient DAX calculations can slow down Power BI reports. Follow these tips to optimise visuals and formulas:

Limit the Number of Visuals per Page: Too many visuals increase the load time. Keep only the necessary visuals on each report page.

Use Simple and Efficient DAX Formulas: Avoid complex DAX functions that require high computational power.

Enable Row-Level Security (RLS) Thoughtfully: While RLS is useful for security, excessive filters may slow down report performance. Use it only when necessary.

6. Use Power BI Service Optimisation Techniques

If you are using Power BI Service, additional optimisations can improve performance:

Use Scheduled Data Refresh Efficiently: Schedule refreshes during non-peak hours to avoid performance bottlenecks.

Optimise Data Storage Mode: Choose between Import, DirectQuery, and Composite mode based on performance requirements.

Enable Performance Analyser: Use the built-in Performance Analyser to identify slow-performing visuals and queries.

Conclusion

Optimising Power BI reports is essential for efficient data analysis and a smooth user experience. By reducing dataset size, using aggregations, and optimising data models, users can significantly improve report performance. Whether you are a professional working with Power BI or someone pursuing a Data Analyst Course or considering a Data Analyst Course in Pune, learning these performance optimisation techniques will give you a competitive edge in data analysis and visualisation.

At ExcelR, we equip aspiring data analysts with industry-relevant skills, including Power BI optimisation techniques, to help them succeed in the evolving field of data analytics. Enroll today and take your Power BI expertise to the next level!

Contact Us:

Name: Data Science, Data Analyst and Business Analyst Course in Pune

Address: Spacelance Office Solutions Pvt. Ltd. 204 Sapphire Chambers, First Floor, Baner Road, Baner, Pune, Maharashtra 411045

Phone: 095132 59011

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Take advantage of tech

Microsoft's business intelligence tools offer a comprehensive suite for data analysis, visualisation, and reporting, empowering organisations to make data-driven decisions. Power BI stands as the flagship product, providing interactive dashboards and reports that connect to various data sources, from spreadsheets to cloud-based databases. Excel, with its robust analytical capabilities and Power Pivot/Power Query add-ins, remains a powerful tool for data manipulation and analysis. SQL Server Analysis Services (SSAS) provides a platform for building enterprise-grade semantic data models, enabling complex calculations and data exploration. Azure Analysis Services extends these capabilities to the cloud. Additionally, tools like Azure Synapse Analytics facilitate large-scale data warehousing and analytics, enabling organizations to process and analyse vast datasets for deeper insights. These tools collectively provide a scalable and integrated BI ecosystem for businesses of all sizes.

[scmsnoida]

Top Career Paths After a BBA in Business Analytics

Companies depend on data-driven decision-making to remain competitive in the modern digital era. Business analytics is revolutionising industries worldwide, from forecasting consumer actions to maximising investment returns. A BBA in Business Analytics prepares students to read data, identify patterns, and offer actionable insights that help businesses thrive.

At a BBA college in Noida, students get practical data analysis, visualisation, and strategic decision-making training, equipping them for high-growth professions. If you want to know what opportunities lie after this degree, here are your best career choices.

1. Data Analyst

A Data Analyst translates complex data sets to make informed decisions. The finance and e-commerce sectors depend on data analysts to determine trends. They enhance efficiency and provide a better customer experience.

Real-World Example: An SCMS Noida alumnus joined a fintech company, applying data analytics to analyse customer spending patterns and enhance financial products.

2. Business Intelligence Analyst

A Business Intelligence (BI) Analyst converts raw data into insights using Power BI and Tableau software. This is the most important job in companies that want to optimise operations and boost profitability.

Industry Impact: A retail company in Noida maximised its sales strategy using BI dashboards, generating revenue growth of 20%.

3. Marketing Analyst

Marketing Analysts research consumer behaviour, campaign performance, and market trends. As digital marketing takes centre stage, companies require specialists who can optimise marketing strategies and yield high ROI.

Internship Highlight: A student from a BBA college in Noida interned with a digital marketing company, applying analytics to drive SEO rankings and ad conversions.

4. Financial Analyst

Financial Analysts translate market trends, estimate risks, and inform investment strategies. They function in banks, investment banks, and corporate financial departments to increase profitability while decreasing risk.

Success Story: Today, an SCMS Noida graduate assists a stock brokerage firm in optimising investment portfolios through predictive analytics.

5. Operations Analyst

Operations Analysts enhance supply chain effectiveness and recognise bottlenecks in business procedures. The function is highly valued in e-commerce and manufacturing businesses.

Case Study: An Operations Analyst helped an e-commerce company based in Noida minimise delivery times by 30%. 

6. HR Analytics Specialist

HR Analytics Specialists examine employees' data for better recruitment, retention, and workplace culture. Most IT companies and MNCs hire HR analysts to strengthen data-driven teams.

‍Intern in the Spotlight: A student of SCMS Noida interned at a technology firm, assisting them in optimising workforce planning through analytics.

Conclusion

The demand for Business Analytics professionals is skyrocketing across industries. Whether you aim to join a top MNC, work in finance, or start your own business, a BBA in Business Analytics from SCMS Noida gives you the perfect launchpad for success.

If you are ready to build a future-proof career, now is the time to embrace the power of data!

[datavisualisationservices]

Choosing the Right Data Visualisation Solution for Your Business Needs

Introduction

Businesses rely heavily on data visualisation solutions to extract meaningful insights and facilitate informed decision-making in the modern data-driven world. However, selecting the right solution can be overwhelming with the vast array of data visualisation tools available. A well-chosen visualisation tool can enhance business efficiency, improve collaboration, and provide deeper insights.

Understanding Data Visualisation Solutions

Data visualisation solutions transform raw data into graphical representations such as charts, graphs, dashboards, and maps. These solutions help businesses interpret complex data patterns, trends, and correlations more effectively. Choosing the right tool requires a thorough understanding of business objectives, data complexity, and user requirements.

Key Considerations When Choosing a Data Visualisation Solution

1. Business Objectives and Use Cases

Before selecting a tool, businesses must define their primary use cases. Are they looking to improve internal reporting, track key performance indicators (KPIs), or create real-time interactive dashboards? Understanding these objectives will narrow down suitable options.

2. Ease of Use and User Experience

A data visualisation solution should be user-friendly, catering to both technical and non-technical users. A tool with an intuitive interface and drag-and-drop functionalities can empower business users to generate insights without extensive technical expertise.

3. Integration with Existing Systems

The chosen tool must integrate seamlessly with existing databases, business intelligence (BI) platforms, and cloud services. Compatibility with data sources such as SQL databases, spreadsheets, and cloud applications ensures smooth data flow.

4. Customisation and Scalability

Businesses should consider whether the tool can be customised to meet specific needs and scale as the company grows. A scalable solution will support increasing data volumes and more complex analytics over time.

5. Advanced Analytical Capabilities

Some businesses require more than basic visualisation; they need predictive analytics, artificial intelligence (AI)-driven insights, and machine learning capabilities. Selecting a tool that provides these features can give businesses a competitive edge.

6. Collaboration and Sharing Features

For organisations with multiple teams, a tool that allows collaborative data exploration, report sharing, and real-time updates is essential. Cloud-based solutions enhance teamwork and remote access.

7. Security and Compliance

Data security is a top priority. Businesses must evaluate whether the solution meets compliance regulations (such as GDPR or HIPAA) and offers encryption, role-based access control, and secure authentication methods.

8. Cost and ROI Consideration

While budget constraints are important, businesses should weigh the cost against potential benefits. A higher-priced tool with robust features may offer greater long-term value compared to a cheaper, limited-scope solution.

Popular Data Visualisation Tools in the Market

Several tools cater to different business needs. Some of the most popular include:

Tableau – Best for advanced analytics and interactive dashboards.

Power BI – Ideal for Microsoft ecosystem users and enterprise-level reporting.

Google Data Studio – Great for free, cloud-based reporting and collaboration.

D3.js – Suitable for developers who need custom visualisation capabilities.

Looker – Best for integrating BI and data analytics into workflows.

Conclusion

Choosing the right data visualisation solution requires careful evaluation of business needs, technical requirements, and budget. By considering factors such as ease of use, scalability, integration, and security, businesses can select a tool that maximises the value of their data. Investing in the right data visualisation solution empowers organisations to turn raw data into actionable insights, driving business growth and success.

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In today's data-driven world, the ability to analyse and interpret data is a highly sought-after skill across various industries. For those in Noida looking to embark on a career in data analytics, GRAStech offers a comprehensive course designed to equip students with the necessary knowledge and practical experience to excel in this field.

Why Choose GRAStech for Data Analytics Training in Noida?

Comprehensive Curriculum: GRAStech's data analytics course covers a wide range of topics, including data exploration, cleaning, visualisation, and analysis techniques. Students gain hands-on experience with popular tools and programming languages such as Python and SQL, enabling them to extract insights from both structured and unstructured data.

Experienced Instructors: The institute boasts a team of seasoned professionals with extensive experience in data analytics. Their industry insights and practical knowledge provide students with a deep understanding of real-world applications.

Flexible Learning Options: Recognising the diverse needs of learners, GRAStech offers both online and offline training modes. This flexibility ensures that students can choose a learning format that best suits their schedules and learning preferences.

Certification and Career Support: Upon successful completion of the course, students receive a certification from the National Skill Development Corporation (NSDC), enhancing their professional credentials. Additionally, GRAStech provides robust career support, including job placement assistance, interview preparation, and resume building, to help students secure positions in top companies.

Hands-On Projects and Real-World Applications: The course emphasises practical learning through real-world case studies and projects. This approach ensures that students not only understand theoretical concepts but also know how to apply them in practical scenarios, making them job-ready upon graduation

Course Highlights

Duration: The data analytics course spans four months, providing an in-depth exploration of the subject matter.

Modules Covered:

Excel Basics to Advanced

SQL Server

Python Programming

Data Visualization with Power BI

Statistical Data Analysis

Machine Learning Basics

Live Training Sessions: Both live and recorded sessions are available, ensuring that students can learn at their own pace and revisit complex topics as needed.

Affordable Pricing: The course fee ranges from INR 22,000 to INR 25,000, depending on the mode of instruction (online or offline), making it accessible to a wide range of students.

Student Testimonials

Many students have benefitted from GRAStech's data analytics course. For instance, Riya Bhardwaj shares, "I'm doing the Data Analytics course here. Ms. Neeta is exceptionally knowledgeable. Her clear explanation and real-world examples make complex concepts easy to understand. Her passion for teaching is truly inspiring.

For more information or to enroll, visit GRAStech's official website:

grastech.in

[ajay--sharma]

Power BI has many features to help with analyzing and visualising data in the format chosen by the user. Here are some of the key features of Power BI:

Data Connectivity: Using Power BI it is possible to connect to a wide range of data sources, including cloud services and local databases.

Real-time Data Refresh: It provides features for refreshing the data in real time.

Interactive Visualizations: The software allows users to create and share interactive and immersive reports and dashboards with multiple visualizations.

Natural Language Queries: Another capability being offered to business users is the natural language queries through which users can ask questions and get responses in form of charts and graphs.

Data Modeling: It has comprehensive data modeling capabilities that support the handling of large amounts of data and provides tools to change the data when necessary.

Customizability: Power BI provides users with a flexible option to design the layout of the dashboard or a report according to the organization needs.

Integration with Other Microsoft Products: It is well aligned with other Microsoft business apps like Microsoft Azure and Office 365 for the interoperation of data.

Data Security: Possesses strong security measures at various tiers, including the option to regulate data’s accessibility and use.

AI Support: Azure AI is integrated with Power BI to provide users with direction within data to make better decisions.

Collaboration Tools: Provides options for sharing and publishing of dashboards and reports through the Power BI service. Learn More:-https://www.analyticsshiksha.com/blog/what-is-power-bi-data-analysis-features-uses-benefits