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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.

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Best Data Science Course in Laxmi Nagar

Data science is an interdisciplinary and emerging field. It combines mathematics and computing to analyse large and complex data sets. Our degree will give you the skills to turn data into meaningful outcomes. You'll learn to use your analytical and computational skills to interpret, clean, and visualise data confidently. You'll graduate with expertise in the most relevant and innovative data science topics. You'll learn and work alongside leading practitioners in data science - data science and data analytics course.

This program introduces students to different techniques and tools for handling, managing, analyzing, and interpreting data. It is designed for working professionals who are interested in gaining hands-on experience working with data and analyzing it. This program primarily focuses on data management and analysis with introductions to tools such as Oracle SQL, Tableau, Python etc. After completing the program, the students will have a solid foundation for applying advanced statistics and quantitative methods to make informed, effective decisions - Best data science institute in delhi.

The program has also successfully made several collaborations with different institutions and companies that can benefit from it. Students who opt for this program have the option to transfer credits for Master’s programs with top-ranked universities and gain lateral entry int sem directly. Overall, this is an ideal data science course for beginners as the instructors let you begin with the basics and build up as you progress. When embarking on your data science education journey, evaluate your skill levels, career goals and timelines to pick courses aligning to your learning needs. You’ll also be able to use SQL effectively for targeted analysis by interpreting the structure, meaning, and relationships within the data.

You will engage with a variety of learning materials, such as videos, assessments, and hands-on labs, to familiarize yourself with the tools and analytical skills needed to work with data. Attendance is not just about the student’s willingness to learn but is also indicative of how the program is conducted to keep the student’s attention. For more information, please visit our site https://eagletflysolutions.com/ 

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Free Data Science Courses to Build Your Career Skills

Introduction

Data science is transforming industries, making it one of the most in-demand skills in today's job market. With the global data science platform market projected to grow from $95.3 billion in 2021 to $322.9 billion by 2026 at a staggering CAGR of 27.7%, professionals with data expertise are in high demand. 

Free data science courses provide an excellent opportunity to upskill without financial barriers. They enable you to learn data science for free, covering everything from basic concepts to advanced techniques. This blog highlights top free resources, helping you kickstart or advance your career in this rapidly growing field.

Top Free Data Science Courses

Data Science is evolving rapidly, and finding quality resources for learning without paying is now easier than ever. Whether you’re new to Data Science or looking to expand your knowledge in specialised areas like Machine Learning or Data Visualisation, free online courses offer a fantastic opportunity to learn at your own pace. 

Here’s a comprehensive guide to the top free Data Science courses available. Each course provides a unique focus, giving you options to build your Data Science skills effectively and affordably.

Applied Data Science by Future Learn

Future Learn’s Applied Data Science course collaborates with Coventry University, the Institute of Coding, and Birkbeck University to introduce students to the practical aspects of Data Science. This beginner-friendly course emphasises Data Visualisation, Machine Learning applications, and clustering techniques.

Key Features

User-Friendly Platform: Future Learn offers an intuitive experience with flexible schedules and engaging video-based learning.

Focus on Applied Data Science: Practical examples help users apply Data Science methods to real-world business cases.

Self-paced Learning: Students can start anytime and learn at their speed.

Certificate Option: Obtain a certificate for an additional 6,222 ₹.

Accessible Topics: Covers geolocation data, feature extraction, and introductory Machine Learning.

Data Science Professional Certificate by IBM on Coursera

The IBM Data Science Professional Certificate on Coursera offers an in-depth curriculum spanning nine courses. This specialisation takes about 11 months to complete. It includes a capstone project designed to simulate real-world data challenges, making it one of the most comprehensive free courses in Data Science.

Key Features

Nine-Course Structure: Covers everything from basic Data Science concepts to advanced SQL and Python.

Capstone Project: Provides a simulated real-world experience.

Certificate Upon Completion: Receive a shareable certificate on Coursera upon successful completion.

Excellent Instruction Quality: Taught by experienced IBM professionals.

Career-Boosting Potential: Many students use this course as a launchpad for Data Science careers.

Data Science Course by Pickl.AI

Pickl.AI’s Data Science Course is structured into 11 modules, covering everything from SQL and Tableau to Machine Learning techniques. With hands-on exercises and real-world case studies, this course is designed for beginners and professionals looking to advance in Data Science.

Key Features

Extensive Modules: Includes 462 lessons on SQL, Python, Machine Learning, and more.

Real-Life Case Studies: Learn through practical examples and case studies.

Certification Available: Certification offered upon completion, enhancing employability.

Focus on Data Science Tools: Access high-demand tools like Tableau and Power BI.

Flexible Pace: Allows learners to move through modules at their own speed.

Introduction to Data Science by Alison

Alison’s Introduction to Data Science course is a quick, impactful introduction to core Data Science concepts. It’s highly rated among working professionals and provides insights into data structuring, Machine Learning basics, and essential Data Science processes.

Key Features

Quick Overview: Completes in 1.5 to 3 hours, ideal for busy professionals.

Beginner-Friendly: No prior knowledge is required, making it accessible to everyone.

Certification Included: Receive a certificate upon completion, adding value to resumes.

Focus on Practical Skills: Learn data extraction, modelling, and scientific methods.

User-Friendly Platform: Alison is known for its ease of use and career-focused course design.

Data Science and Machine Learning with Python by Skillshare

Skillshare’s Data Science and Machine Learning with Python course offers an immersive look into Python, regression models, and ensemble learning, with a two-week free trial for new users. Frank Kane, an expert with over nine years of experience at Amazon and IMDB, leads this course, making it an excellent option for in-depth Python training.

Key Features

Access with Free Trial: Skillshare’s two-week free trial allows full access to the course.

Professional Instructor: Frank Kane brings practical experience and industry insights.

In-Depth Python Training: Covers foundational Python skills and data manipulation.

Beginner-Friendly: No prior knowledge is needed to start.

10-Hour Format: Perfect balance of depth and accessibility, making it manageable for beginners.

Conclusion

Free data science courses provide an excellent pathway to upskilling and breaking financial barriers. With platforms like Future Learn, IBM, and Pickl.AI offering specialised modules, learners can grasp core concepts, explore advanced techniques, and boost their career potential. Start today and embrace the growing demand for data science skills in this dynamic, evolving industry.

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Journey today and open avenues to endless opportunities in the world of data science

In today’s data-driven world, the demand for skilled data science professionals is at an all-time high. Pune, often referred to as the "Oxford of the East," has emerged as a major hub for education and technology, making it an ideal city for aspiring data scientists. ExcelR’s Data Science course in Pune equips individuals with the knowledge and skills needed to excel in this dynamic field.

Why Choose ExcelR for Data Science in Pune?

ExcelR stands out as a leading institute offering comprehensive training in data science. With a reputation for delivering high-quality education, ExcelR combines theoretical knowledge with practical exposure, ensuring that students are well-prepared for real-world challenges. The course is designed by industry experts and caters to both beginners and experienced professionals looking to upskill.

Comprehensive Curriculum

The curriculum of ExcelR’s Data Science course is meticulously designed to cover all the key aspects of data science, including:

Programming Languages: Proficiency in Python and R, the most widely used languages in data science.

Statistics and Mathematics: Understanding statistical concepts, probability, and linear algebra essential for data analysis.

Machine Learning: Hands-on training in supervised and unsupervised learning algorithms.

Data Visualisation: Tools like Tableau and Power BI to present insights effectively.

Big Data Technologies: Exposure to Hadoop and Spark to handle large-scale data processing.

Artificial Intelligence and Deep Learning: An intro to neural networks and AI applications.

The program also includes case studies, industry projects, and hackathons to provide practical experience.

Experienced Faculty

ExcelR prides itself on having a team of experienced trainers who bring loads of industry knowledge. These instructors are subject matter experts and are also skilled in mentoring students, ensuring that each participant gains a deep understanding of concepts.

Flexible Learning Options

Understanding the diverse needs of learners, ExcelR offers flexibility in its course delivery. Students can choose from classroom training at the Pune centre or opt for online sessions that provide the same level of engagement and interaction. Weekend batches are also available for working professionals.

State-of-the-Art Infrastructure

ExcelR’s Pune centre is equipped with modern facilities to create a conducive learning environment. From well-equipped classrooms to access to the latest software tools, every aspect is designed to enhance the learning experience.

Placement Assistance

One of the standout features of ExcelR’s Data Science course is its placement support. The dedicated placement team provides assistance with resume building, interview preparation, and connecting students with top companies in Pune and beyond. ExcelR’s collaboration with leading organisations ensures ample opportunities for its graduates.

Thriving Data Science Ecosystem in Pune

Pune’s growing tech ecosystem is home to numerous IT companies, startups, and research institutions, creating a high demand for skilled data scientists. By enrolling in ExcelR’s Data Science training program, students can access a network of field experts and potential employers in the region.

Conclusion

ExcelR’s Data Science course in Pune is more than just an educational program; it’s a career-launching platform. With its comprehensive curriculum, expert faculty, and strong industry connections, ExcelR empowers students to thrive in the ever-evolving world of data science. Whether you are a fresher looking to start your career or a professional aiming to transition into data science, ExcelR in Pune is your go-to destination.

Begin your journey today and open avenues to endless opportunities in the world of data science.

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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Unlocking Opportunities with Data Analyst Classes in Hyderabad

Introduction

In the age of big data, the role of a data analyst has become increasingly vital. Companies rely on data analysts to interpret data and provide insights that drive decision-making. Enrolling in data analyst classes or a data analytics course in Hyderabad can lead to a rewarding career for individuals residing in Hyderabad or considering relocation to this tech hub. This blog will explore the benefits and opportunities provided by these courses.

Data Analyst Classes: The First Step Towards a Data-Driven Career

A data analyst class aims to equip you with the essential skills required for effective data analysis and interpretation. These classes cover a range of essential topics, including:

Data Manipulation: Learn how to clean and organise data to make it usable.

Statistical Analysis: Understand key statistical concepts and how to apply them.

Data Visualisation: Master tools like Excel, Tableau, and Power BI to present data clearly and compellingly.

SQL and Database Management: Gain proficiency in SQL to query and manage databases.

These classes are ideal for beginners and those looking to solidify their understanding of data analysis basics.

Specialized Training: Data Analyst Course in Hyderabad

For a more in-depth exploration, a data analyst course in Hyderabad offers comprehensive training tailored to industry needs. This course typically includes:

Advanced Data Techniques: Learn sophisticated methods for data analysis and interpretation.

Programming for Data Analysis: Gain expertise in programming languages such as Python and R.

Machine Learning Basics: An introduction to machine learning concepts and their application in data analytics.

Real-World Projects: Engage in practical projects that simulate real business scenarios to apply what you've learnt.

Enrolling in a data analyst course in Hyderabad provides the skills and knowledge needed to excel in various industries, from finance to healthcare.

Data Analytics Course in Hyderabad: Bridging the Gap Between Education and Employment

Hyderabad is home to many leading tech companies and startups, making it an ideal location for a data analytics course. These courses aim to equip you with the necessary skills for the local job market.

Industry-Relevant Curriculum: Courses developed in collaboration with industry experts to meet current job market demands.

Experienced Instructors: Learn from professionals with extensive experience in data analytics.

Networking Opportunities: Connect with peers and industry leaders through course-related events and workshops.

A typical data analytics course in Hyderabad covers the following topics:

Data Analysis Techniques: In-depth training in various data analysis methods.

Business Intelligence Tools: Hands-on experience with tools like Power BI and Tableau to create insightful business reports.

Capstone Projects: Real-world projects that showcase your skills to potential employers.

Conclusion

Whether you're just starting out or looking to advance your career, enrolling in data analyst classes, a data analytics course in Hyderabad, or a specialized data analyst course in Hyderabad can provide you with the skills and knowledge needed to thrive in the data-driven world. These courses offer a blend of theoretical knowledge and practical experience, ensuring that you are well-prepared to meet the demands of the industry. Invest in your future by choosing the right educational path in Hyderabad and unlock a world of opportunities in data analytics.

Data Science, Data Analyst and Business Analyst Course in Hyderabad

Address: 8th Floor, Quadrant-2, Cyber Towers, Phase 2, HITEC City, Hyderabad, Telangana 500081Ph: 095132 58911

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Data Analyst Course in Chandigarh

The Data Analyst Course at ThinkNEXT Technologies Private Limited in Chandigarh equips students with the essential skills to excel in data analysis. This comprehensive program covers data collection, processing, visualisation, and interpretation using industry-relevant tools like Python, Excel, SQL, and Tableau. Whether a beginner or an experienced professional, this course offers hands-on training, real-world projects, and expert guidance to help you become a proficient data analyst. With a focus on practical learning and career growth, ThinkNEXT prepares you for a successful career in the ever-evolving world of data analytics.

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Data Analytics Course - iNeuBytes

Learn Data Analytics with iNeuBytes’ Advanced Data Analytics Course

Master the art of Data-driven decision-making with iNeuBytes’ Advanced Data Analytics Course. organized for beginners and professionals like this general plan equips you with important psychoanalyst skills and hands-on get-exploitation name tools such as arsenic surpass sql tableau vivant force bismuth and python. Data analytics is decisive in today's digital age, driving business growth and innovation across industries. By enrolling in the advanced Data analytics line from Ineubytes, you'll read to control analyse and see Data Revolutionizing green Data into significant Understandings that get determine line strategies

The line covers everything from the bedrock of surpass and sql for Database direction to advance Data visualisation techniques exploitation tableau vivant and force bismuth. You also dive deep into Python programming, a difficult skill for Data analysts. with real-world projects good instructors and industry-aligned happiness, the advanced Data analytics line ensures you clear the skills necessary to surpass in-plus high-demand career

Boost your vocation prospects and bear away the aggressive work grocery with ineubytes' certificate. Enroll in the Advanced Data Analytics Course today and become a proficient Data analyst ready to tackle real-world challenges.

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A Comprehensive Guide to the Best Data Analytics Certification Courses in India

Summary: In-depth Data Analysis knowledge in today's digital age is crucial for driving strategic decisions and innovation. Explore comprehensive programs designed for professionals, build foundational skills, and enhance your career. Learn about the best analytics courses, success stories, and criteria for selecting the right course to advance your Data Analytics journey.

Introduction

In the age of big data, the role of Data Analytics has become increasingly pivotal in driving business decisions and strategies. For those looking to enter or advance in this dynamic field, numerous online certification courses offer flexible and high-quality education. This detailed guide explores the top Data Analytics online certification courses in India, helping you make informed decisions to boost your career.

What is Data Analytics?

Data Analytics involves the systematic computational analysis of data to uncover patterns, correlations, and trends. It helps organisations make informed decisions, optimise processes, and predict future outcomes. Data analysts use various tools and techniques to clean, process, and interpret data, providing actionable insights that drive business strategy and efficiency.

Importance of Data Analytics

The importance of Data Analytics cannot be overstated in the modern business environment. Key benefits include:

Informed Decision-Making: Helps businesses make data-driven decisions to improve outcomes.

Efficiency and Optimization: Streamlines operations by identifying inefficiencies and areas for improvement.

Customer Insights: Enhances understanding of customer behaviour, leading to better customer experiences and retention.

Competitive Advantage: Provides insights that can give companies a competitive edge in the market.

Criteria for Evaluating Online Certification Courses

Evaluating online Data Analytics certification courses? Consider factors like course content, instructor expertise, hands-on projects, industry relevance, and post-course support to ensure you make an informed decision for your professional development.

Curriculum: Ensure the course covers essential topics and tools relevant to Data Analytics.

Instructor Expertise: Look for courses taught by experienced professionals or reputable institutions.

Hands-On Projects: Practical experience is vital. Courses should include real-world projects and assignments.

Flexibility: Opt for courses that offer flexible schedules to accommodate your commitments.

Certification and Recognition: The course should provide a recognized certification upon completion.

Student Support: Access to mentors, career support, and a community of learners can enhance your learning experience.

Top Data Analytics Online Certification Courses in India

Explore the best online Data Analytics certification courses in India, designed to equip you with essential skills for a data-driven career. Discover top institutions and programs tailored for aspiring data professionals.

1. Coursera - Google Data Analytics Professional Certificate

Best analytics courses Offers a well-rounded foundation in Data Analytics with industry-recognized certification. This beginner-friendly program covers data cleaning, analysis, and visualisation using tools like SQL, R, and Tableau.

Duration: Approximately 6 months (10 hours per week)

2. edX - Data Analytics for Business by Wharton School, University of Pennsylvania

This course is considered to be the best for its academic rigour and business-focused approach. Focuses on data-driven decision-making in a business context, using Excel, Tableau, and SQL.

Duration: Approximately 3 months (4-6 hours per week)

3. Simplilearn - Post Graduate Program in Data Analytics

Post Graduate Program in Data Analytics by Simplilearn, offers comprehensive programs with industry relevance and hands-on projects. In collaboration with Purdue University and IBM, covers essential analytics skills using Excel, R, Python, Tableau, and SQL.

Duration: 12 months

4. Great Learning - PG Program in Data Science and Business Analytics

It offers extensive learning in both data science and business analytics with a focus on practical skills.Collaborating with the McCombs School of Business at the University of Texas at Austin,, covers machine learning, data visualisation, and business analytics.

Duration: 11 months

5. UpGrad - Data Science Certificate Program by IIIT Bangalore

This course includes a strong program with a focus on data science principles and real-world applications.Covers Data Analysis, machine learning, and data visualisation using Python, R, and Tableau.

Duration: 12 months

6. Pickl.AI - Data Analytics Certification Program

If you are looking for the best analytics courses, consider enrolling for Pickl.AI’s Data Analytics certification course. It provides essential skills in Data Analytics with practical project experience.

Data Analytics Certification Program provides a comprehensive introduction to Data Analytics, including data cleaning, exploratory analysis, and visualisation techniques. The course emphasises practical skills with real-world projects, focusing on tools such as Python and SQL.

Duration: Flexible, typically completed in 3 to 6 months depending on the learner’s pace.

7. NPTEL -Data Analytics with Python

It is one of the best analytics courses in India. The course equips you with foundational knowledge in analytics along with Python, foundational knowledge and Python-based analytics This affordable course offers a comprehensive introduction to Data Analytics using Python.

Duration: 8 weeks

Read More: Data Analyst Interview Questions and Answers.

Developing Foundational Skills

Enrolling in top Data Analytics courses helps build foundational skills such as data cleaning, statistical analysis, and data visualisation. Mastery of tools like Python, R, SQL, and Tableau is crucial for success in the field. These skills are essential for both academic success and practical application in various industries.

Conclusion

The field of Data Analytics offers immense opportunities, and obtaining a certification can be a pivotal step in your career. With numerous high-quality online certification courses available in India, you can choose the one that best fits your needs and schedule. 

Whether you are just starting out or looking to advance your career, these courses provide the knowledge and skills necessary to succeed in the dynamic field of Data Analytics. Choosing the right data analytics course can be transformative, setting you on a path to success in one of the most in-demand fields today.

Frequently Asked Questions

What are the Benefits of Obtaining a Data Analytics Certification in India?

Obtaining a Data Analytics certification in India enhances your employability by signaling your expertise to potential employers. Certified candidates often have better job prospects and higher salary offers. Platforms like Pickl.AI Google and IBM’s certifications provide practical skills in data analysis, making graduates more competitive in the job market.

Are there Prerequisites for Enrolling in Data Analytics Certification Courses?

Most Data Analytics certification courses in India do not require prior experience. However, a basic understanding of statistics and familiarity with data concepts can be beneficial. Courses are designed for beginners, allowing anyone interested in Data Analytics to start their learning journey without extensive background knowledge.

What is the Typical Duration and Cost of Data Analytics Certification Courses in India?

The duration of Data Analytics certification courses in India varies, typically ranging from 3 to 11 months, depending on the program. Costs can range from ₹500 to ₹1,00,000, depending on the course level and institution. Online platforms like Pickl.AI offer flexible pricing options to accommodate different budgets.The duration of Data Analytics certification courses in India varies, typically ranging from 3 to 11 months, depending on the program. Costs can range from ₹500 to ₹1,00,000, depending on the course level and institution. Online platforms like Pickl.AI offer flexible pricing options to accommodate different budgets.

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Professional Data Analytics Course in-depth in Rohini

The significance of data analytics in today's data-driven environment cannot be emphasised. Businesses in a variety of sectors use data analytics to improve operations, make wise decisions, and obtain a competitive edge. Consequently, being skilled in data analytics has become very desirable. Aspiring professionals who want to work in the field of data analytics can use this article as a thorough guide.

Comprehending Data Analytics Analysing datasets to make inferences about the information they hold is known as data analytics. Data analysis, which is a crucial component of many corporate operations, including supply chain management, marketing, and finance, requires the use of specialised systems and software.

Data analytics encompasses several key components:

Data Collection: Gathering relevant data from various sources.

Data Cleaning: Ensuring the data is accurate and usable by removing errors and inconsistencies.

Data Analysis: Applying statistical and computational techniques to extract insights.

Data Interpretation: Understanding and communicating the significance of the analyzed data.

Data Analytics Types Four main categories of data analytics exist: Using historical data, descriptive analytics describes what has happened in the past. The simplest type of analytics includes summarising data in order to identify trends or patterns. Investigates the causes of historical occurrences using diagnostic analytics. In order to comprehend cause-and-effect relationships and the reasons behind specific outcomes, it digs deeper into the data. Predictive analytics forecasts future events by utilising statistical algorithms and past data. This kind uses methods like data mining and machine learning to forecast patterns and actions. Prescriptive analytics: Makes suggestions for actions based on information. It not only forecasts future events but also recommends the optimal path of action to take in order to reach the intended results.

Join our comprehensive Data Analytics course in Rohini Gain hands-on experience with tools like Python, R, and Tableau. Learn data collection, cleaning, analysis, and visualization techniques. Ideal for beginners and professionals seeking to enhance their skills. Unlock career opportunities in a data-driven world with expert guidance and practical projects. Enroll now to transform your data proficiency!

The Importance of Data Analytics

Data analytics is crucial for several reasons:

Informed Decision-Making: By analyzing data, organizations can make more informed decisions, minimizing risks and uncertainties.

Operational Efficiency: Analytics can identify inefficiencies and areas for improvement, leading to optimized operations and reduced costs.

Customer Insights: Understanding customer behavior and preferences allows for better-targeted marketing and improved customer satisfaction.

Competitive Advantage: Companies that effectively use data analytics can gain a significant edge over competitors who do not.

Essential Knowledge for Data Analysts Aspiring professionals in data analytics require a blend of hard and soft skills to thrive. Among the most crucial abilities are the following:

Statistical Analysis: Analysing data and coming to relevant findings requires a basic understanding of statistical procedures. Programming: To manipulate and analyse data, one must be proficient in languages like Python, R, and SQL. Data visualisation: Effective communication of insights requires the capacity to show data in an aesthetically pleasing and comprehensible manner. Commonly used tools include Tableau, Power BI, and D3.js. Machine Learning: Prescriptive and predictive analytics require an understanding of machine learning techniques and algorithms. Critical Thinking: Data interpretation and strategic decision-making depend heavily on one's capacity for critical thought and problem-solving.

Tools and Technologies in Data Analytics

A variety of tools and technologies are available to assist data analysts in their work. Some of the most commonly used include:

Excel: A fundamental tool for data analysis and manipulation.

SQL: Essential for querying and managing databases.

Python and R: Popular programming languages for data analysis and machine learning.

Tableau and Power BI: Leading tools for data visualization.

Hadoop and Spark: Frameworks for handling big data.

TensorFlow and PyTorch: Libraries for machine learning and deep learning.

The Data Analytics Process

The data analytics process typically involves several key steps:

Define the Objective: Clearly articulate the problem or question to be addressed through data analysis.

Data Collection: Gather relevant data from various sources, ensuring it is accurate and comprehensive.

Data Cleaning: Clean the data to remove any errors, inconsistencies, or missing values.

Data Exploration: Explore the data to understand its structure, identify patterns, and determine which techniques to apply.

Data Modeling: Build models to analyze the data and extract insights. This may involve statistical analysis, machine learning, or other techniques.

Interpretation: Interpret the results to draw meaningful conclusions and provide actionable recommendations.

Communication: Communicate findings to stakeholders in a clear and compelling manner, often through visualizations and reports.

Case Studies in Data Analytics

Case Study 1: Netflix

Netflix is a prime example of a company that has successfully leveraged data analytics to drive its business. By analyzing viewing habits and preferences, Netflix can recommend personalized content to its users. This has led to increased viewer engagement and customer retention. Additionally, data analytics helps Netflix decide which content to produce, ensuring high audience satisfaction and return on investment.

Case Study 2: Walmart

Walmart uses data analytics to optimize its supply chain and inventory management. By analyzing sales data and customer purchasing patterns, Walmart can predict demand and manage stock levels more efficiently. This reduces costs and improves customer satisfaction by ensuring products are available when and where customers need them.

Case Study 3: Healthcare

In the healthcare sector, data analytics is used to improve patient outcomes and operational efficiency. For example, predictive analytics can identify patients at risk of developing certain conditions, allowing for early intervention and better management of chronic diseases. Hospitals also use data analytics to streamline operations, reduce wait times, and allocate resources more effectively.

Future Trends in Data Analytics

The field of data analytics is continuously evolving, with several emerging trends shaping its future:

Artificial Intelligence and Machine Learning: AI and machine learning are becoming increasingly integrated into data analytics, enabling more advanced and accurate predictive models.

Big Data: The growing volume of data generated by digital devices and IoT presents both challenges and opportunities for data analytics. Advanced tools and techniques are being developed to handle and extract insights from big data.

Real-Time Analytics: Real-time data analytics is gaining prominence, allowing organizations to make immediate, data-driven decisions.

Data Privacy and Ethics: With the increasing focus on data privacy and ethical use of data, organizations must ensure compliance with regulations and adopt best practices for data governance.

Self-Service Analytics: Tools that enable non-technical users to perform their own data analysis are becoming more popular, democratizing access to data insights.

Conclusion

Data analytics is a powerful tool that drives decision-making and innovation across industries. As the demand for data-driven insights continues to grow, the need for skilled data analysts becomes increasingly critical. By understanding the fundamentals of data analytics, developing key skills, and staying abreast of emerging trends, aspiring professionals can position themselves for success in this dynamic and rewarding field.

The journey to becoming a proficient data analyst involves continuous learning and adaptation. With the right mindset and resources, anyone can harness the transformative power of data analytics to make a significant impact in their organization and beyond.

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Data Analyst Course Near Me, Mumbai

Are you in search of the ideal data analyst course near you in Mumbai? If you're struggling to find the perfect fit, look no further. Explore your potential through our Data Analytics certification course in Mumbai. Our practical curriculum, expertly designed, provides you with the essential skills and knowledge needed to excel in the field. Engage in real-world projects, creating a comprehensive portfolio that impresses employers and establishes your presence in the competitive IT industry. Join us on a journey toward a promising career in data analytics.

Data Analyst Course in Mumbai Overview 

Are you ready to become a data analytics expert? Our collaborative course with IBM is designed to guide you through the essentials of data analysis without the jargon. Learn the latest analytics tools, SQL, and programming languages like R and Python. Gain hands-on experience in creating data visualizations and applying statistical and predictive analytics in real-world business scenarios.

Course Highlights:

Industry-Backed Learning

Practical Skill Development

Versatile Language Training: Master SQL, R, and Python 

Visualization Techniques

Statistical Analysis

Live Sessions by Industry Experts

Certification for Career Advancement

Skills You’ll Learn

Enroll in our online & offline data analytics training course in Mumbai, which covers a wide range of skills, including:

Python

Data Analysis

SQL

Data Visualisation

Advanced Excel

Tools Covered

You will gain proficiency in employing a diverse set of tools and technologies commonly utilized by data analysts, including:

Excel

MySQL

Tableau

Python & R

Business Statistics

Power BI

Data Analyst Course in Mumbai Curriculum

Begin a transformative  journey with our Data Analyst Course in Mumbai, thoughtfully designed to provide essential skills for the dynamic field of data analytics. This hybrid program offers flexibility with both online and offline learning options, ensuring accessibility for all. Our curriculum covers a spectrum of topics, including statistical analysis, data visualization, programming languages like Python and R, database management, and the use of cutting-edge tools such as Tableau and Power BI. Engage in real-world projects to refine practical skills and construct a compelling portfolio. What sets us apart is our commitment to your success. We offer a data analyst course in Mumbai with placement assistance to ensure you're well-prepared for a rewarding career in data analysis. Join us and explore the world of data analytics for a promising professional journey.

1. Advanced Excel

The Advanced Excel course teaches advanced formulas, functions, data visualization, Pivot tables, and charts, enhancing Excel skills for better data handling and analysis in professional roles.

Data Cleaning and Preparation

Formulas and Functions

Pivot tables and Pivot charts

Data Analysis Tools

Power Query

Data Analysis and Power Pivot

Advanced Charting and Visualization

Macros and VBA

Data Analysis with What If Analysis

Reporting and Dashboards

2. Python

This Python course is tailored for data analysts to utilize Python's power for data manipulation, exploration, visualization, and analysis, equipping them with essential skills and libraries for data-driven decision-making.

Python Programming Language

Data Manipulation Libraries

Data Visualization Libraries

Jupyter Notebooks

Data Cleaning and Preprocessing

SQL Integration

Statistical Analysis

Data Analysis Workflow

Web Scraping

NumPy

3. SQL

The SQL course offers a comprehensive understanding of SQL, a fundamental skill for data management and querying relational databases, making it an excellent starting point for beginners.

SQL Basics

Data Manipulation

Data Aggregation and Grouping

Data Joins and Relationships

Subqueries and Derived Tables

Data Cleaning and Preparation

Window Functions (Analytical Functions)

Time Series Analysis

Pivoting and Unpivoting Data

Data Visualization with SQL

4. Tableau/Power BI

This course teaches users how to use Tableau and Power BI to create interactive dashboards, charts, and reports, enhancing their data storytelling skills.d data visualization tools. Data analysts play a crucial role in transforming raw data into meaningful insights, and this course aims to equip them with the skills to create impactful visualizations that facilitate data-driven decision-making. Participants will learn to extract, clean, and visualize data effectively, enabling them to communicate complex information visually and intuitively.

Data Connection and Data Source

Data Transformation and Data Modeling

Data Visualization Basics

Creating Basic Visualizations

Interactive Dashboards

Advanced Visualizations

Calculated Fields and Expressions

Table Calculations

Time Series Analysis

Level of Detail (LOD) Expressions

Data Analyst Certificate

Unlock the potential of data with our comprehensive Data Analyst Certification Course in Mumbai. Earn the valued Develearn Certificate, proving your practical skills and understanding in data analysis. This certification is more than just a badge; it signifies your hands-on expertise and knowledge of essential data analysis concepts, tools, and methods.

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Data Science Tools and Software Overview: Navigating the Essential Toolkit

Introduction

In the multifaceted world of data science, the tools and software used can significantly influence the efficiency and effectiveness of data-driven projects. The right tools can make a difference from data gathering and cleaning to analysis and visualisation. Popular software like Jupyter Notebooks, TensorFlow, and Tableau have become integral in the data scientist's toolkit. Aspirants pursuing a data science course should use these essential tools proficiently. This article provides an overview of these and other essential tools in data science, exploring their functionalities and applications. 

1. Jupyter Notebooks

Jupyter Notebooks, an open-source web application, is widely recognised in the data science community for its versatility and user-friendliness. It enables the creation and sharing documents featuring live code, equations, visualisations, and explanatory text. Its support for over 40 programming languages, primarily Python and R, makes it a favourite for data cleaning, statistical modelling, and machine learning. The interactive environment of Jupyter Notebooks is ideal for exploratory data analysis and collaborative projects.

2. TensorFlow

Developed by Google Brain, TensorFlow is an influential open-source library for NC and ML. It is renowned for its flexibility in building and deploying machine learning models and intense learning models. It primarily facilitates the training and inference of deep neural networks, among other tasks. Its ability to process large volumes of data and compute complex algorithms efficiently makes it a go-to tool for professionals working on cutting-edge AI projects.

3. Tableau

Tableau is a leading data visualisation tool, enabling users to convert raw data into easily understandable, interactive, and visually appealing formats. Its user-friendly drag-and-drop interface allows users to craft complex graphs, charts, and dashboards effortlessly, bypassing extensive coding. Tableau is particularly favoured in business intelligence for its ability to help make data-driven decisions by providing insights into trends, patterns, and correlations.

4. R and Python Programming Languages

R, created explicitly for statistical computing and data analysis, is alongside Python, one of the most favoured programming languages in data science. R boasts an extensive package ecosystem and is highly regarded for its statistical modelling and visualisation capabilities. On the other hand, Python is celebrated for its simplicity and readability, making it accessible to beginners. Its extensive libraries, such as Pandas for data manipulation, NumPy for numerical computing, and Scikit-learn for machine learning, make it incredibly versatile for various data science applications.

5. SQL

SQL (Structured Query Language) is crucial for data science professionals, especially those with large-scale databases. It is used for querying and manipulating databases. Knowledge of SQL is essential for data extraction, transformation, and loading (ETL) processes, which are foundational in data analysis.

6. Apache Hadoop

Apache Hadoop is an open-source platform designed for the distributed handling and processing of vast data sets. It is beneficial for big data analytics. Its ecosystem includes modules like Hadoop Common, HDFS, YARN, and MapReduce, each serving a different purpose in data processing.

7. GitHub

GitHub is more than just a tool for software developers. It serves as a platform for version control and collaboration for data scientists. It allows them to track changes in their code, collaborate on projects, and share their work with others. It is essential for managing complex data science projects and fostering open-source collaboration.

Conclusion

The field of data science is as diverse as the tools it employs, and mastering them is crucial in any data science course. Each tool and software plays a unique role in the data science workflow, from Jupyter Notebooks for code development to TensorFlow for machine learning and Tableau for visualisation. R and Python offer extensive programming capabilities, vital for any comprehensive data scientist course. SQL is indispensable for database management, Hadoop excels in extensive data handling, and GitHub is essential for collaboration. As the field of data science continues to evolve, these tools will enhance capability and grow in importance, shaping the future of data-driven analysis and decision-making. As emphasised in data science courses, proficiency in these tools is beneficial and essential for anyone pursuing a data scientist course.

ExcelR - Data Science, Data Analyst Course Training

Address: 1st Floor, East Court Phoenix Market City, F-02, Clover Park, Viman Nagar, Pune, Maharashtra 411014

Phone Number: 096997 53213

Email Id: enquiry@excelr.com

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Navigating the Data Analyst Learning Path

1. Introduction

In the era of big data, the role of a data analyst has become increasingly crucial across industries. Whether you're a seasoned professional looking to upskill or a newcomer intrigued by the world of data, embarking on the right learning path is essential. This article will guide you through the key steps and resources to follow in order to become a proficient data analyst.

2. Understanding the Basics

Before diving into the complex realm of data analysis, it's imperative to build a solid foundation. Begin by understanding the basics of mathematics and statistics. Concepts like probability, descriptive statistics, and inferential statistics are fundamental tools in a data analyst's arsenal. Online platforms such as Khan Academy, Coursera, and edX offer excellent courses to grasp these foundational concepts.

3. Mastering Excel and Data Manipulation

Excel remains a ubiquitous tool in the data analyst's toolkit. Learn how to efficiently use functions, pivot tables, and charts. Additionally, delve into data manipulation techniques using Excel. Platforms like Microsoft Learn and LinkedIn Learning provide comprehensive courses to enhance your Excel skills.

4. Introduction to Programming

As a data analyst, having programming skills is non-negotiable. Python and R are two of the most widely used languages in the field. Start by learning the basics of these languages on platforms like Codecademy or DataCamp. Understanding programming is essential for data cleaning, analysis, and visualisation.

5. Database Fundamentals

Data is stored in databases, and as a data analyst, you need to be adept at navigating through them. Familiarise yourself with relational databases and SQL, a standard language for managing and manipulating data. Online platforms such as W3Schools and Mode Analytics offer tutorials to get you started on database fundamentals.

6. Data Cleaning and Preprocessing

Data is rarely perfect. Learning how to clean and preprocess data is a critical skill. Platforms like Kaggle and DataCamp provide hands-on exercises and real-world datasets to help you master this art. Understanding how to handle missing values, outliers, and inconsistencies will make you a more effective data analyst.

7. Exploratory Data Analysis (EDA)

EDA involves visually and statistically analysing data sets before diving into formal modelling. Tools like Pandas and Matplotlib in Python are popular for EDA. There are numerous tutorials and courses available on platforms like DataCamp and Towards Data Science to help you master exploratory data analysis techniques.

8. Data Visualization

The ability to communicate insights effectively is a key trait of a successful data analyst. Learn to create compelling visualisations using tools like Tableau, Power BI, or Python libraries like Seaborn and Plotly. Websites like D3.js offer advanced visualisation options for those looking to take their skills to the next level.

9. Introduction to Machine Learning

While not mandatory for all data analysts, a basic understanding of machine learning can set you apart. Platforms like Kaggle, Coursera, and fast.ai offer courses ranging from beginner to advanced levels. Understanding how to apply machine learning algorithms for predictive analytics can significantly enhance your analytical capabilities.

10. Building a Portfolio

Assemble a portfolio showcasing your skills and projects. Share code snippets, visualisations, and insights from your analyses. Platforms like GitHub are ideal for hosting your code, while creating a personal website or using platforms like LinkedIn allows you to showcase your portfolio to potential employers.

Conclusion

Becoming a proficient data analyst involves a systematic approach to learning and mastering various skills. By following this learning path, you'll not only gain a solid foundation in data analysis but also set yourself up for success in a data-driven world. Remember, the key is continuous learning and practical application of your skills in real-world scenarios. Good luck on your journey to becoming a data analyst!

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Unlock The Data Visualisation Tableau Course Expert Guide for Beginners

Whether you're looking to elevate your career, upskill for a promotion, or simply enhance your data visualization capabilities, the Henry Harvin Tableau Course is your ultimate solution. Don't miss this opportunity to learn from the best and upgrade your skills with a reputable certification in Tableau.

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Exploring Advanced Tableau Techniques for Improved Data Insights

Introduction:

Tableau has emerged as a powerful tool for visualizing and interpreting complex datasets in the dynamic world of data analysis. While beginners can quickly grasp the fundamentals of Tableau, mastering advanced techniques can significantly improve your ability to extract valuable insights from your data. In this article, we will delve into the realm of advanced Tableau techniques that will enable you to gain deeper data insights and unlock the full potential of this versatile data visualization tool.

Using Parameters and Calculated Fields: Parameters and calculated fields are essential tools for developing interactive and dynamic dashboards. Learn how to use parameters to provide user-driven data exploration, allowing viewers to adjust variables and instantly see the corresponding changes. Learn how to use calculated fields to perform complex calculations, build custom aggregations, and create sophisticated calculations that go beyond Tableau's built-in functions.

Advanced Data Blending and Joining: Tableau's data blending and joining capabilities extend beyond simple table connections. Learn advanced techniques for merging disparate datasets, dealing with granularities, and dealing with data with common and uncommon dimensions. Investigate data blending with multiple data sources and discover Tableau's true power in combining and visualizing diverse datasets.

Level of Detail (LOD) Expressions: In Tableau, Level of Detail (LOD) expressions allow you to perform calculations at various levels of data granularity. Dive into LOD expressions to perform complex aggregations, create custom dimensions, and generate insights that necessitate data analysis at various levels of detail. Learn how to use LOD expressions' flexibility and precision to answer specific business questions and gain deeper insights.

Advanced Visualisations and Dashboard Design: In addition to basic charts and graphs, Tableau provides a wide range of visualization options. Learn about advanced visualization techniques such as dual-axis charts, heat maps, tree maps, box plots, and more. Learn how to create interactive and user-friendly dashboards by incorporating features such as actions, filters, and parameter controls. Discover best practices for dashboard layout, storytelling, and incorporating interactivity to improve user engagement and comprehension.

Integrating Advanced Analytics: Tableau's integration with statistical and predictive analytics tools opens up a world of advanced analytics possibilities. Learn how to use R and Python scripts in Tableau to perform advanced statistical analysis, predictive modeling, and machine learning. Discover insights that go beyond traditional data visualization by combining the power of Tableau with advanced analytical techniques.

Advanced Mapping and Geographic Analysis: Tableau's mapping capabilities go beyond simple location plotting. Learn advanced geospatial analysis techniques such as spatial aggregations, custom geocoding, and data integration with GIS. Learn how to make interactive maps with layers, tooltips, and filtering options to visualize data in a spatial context and gain geographic insights.

Conclusion: By utilizing these advanced Tableau techniques, you can unlock the full potential of your data and gain deeper insights. Each technique, from parameters and calculated fields to advanced analytics and mapping, enables you to tackle complex data challenges and effectively communicate your findings. Utilize advanced Tableau techniques to turn your data into a compelling visual narrative that drives informed decision-making and uncovers valuable insights.

Are you ready to master Tableau? Join CACMS for a full course to unleash your data visualization abilities. Enroll now at http://cacms.in/

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QlikView Vs Qlik Sense Comparison – Which is Better BI Tool

The demand for business intelligence tools has increased over the past few decades. BI tools are becoming a new source of providing efficient data storage and transportation. Well, many companies have already experienced with Qlik and realized the power of Business Intelligence tools. Whereas if we talk about business intelligence tools, then Power BI and Tableau are also the most popular sought-after tools.

Analyzing the reviews of the company, we can positively say that Qlik delivers some great results to its customers. With Qlik businesses not only gain deeper insight into their business but also analyze, consolidate and search data with ease. We may use Qlik products when we are working in Qlik's companies, but we cannot identify the differences between two of Qlik's essential products, Qlik View and Qlik Sense.

To help you identify those differences, we provide this article, and we cover all the essential differences between Qlik View and Qlik Sense. Let's look in detail.

What is QlikView

QlikView is a guided data analysis and visualization tool that enables users to explore, drill down, fetch, integrate, process and analyze data from various data sources. QlikView allows very limited control for the user to create their own visualizations.

QlikView can be used to develop data models, analytical applications, dashboards, visualizations to create analytical reports and they can be delivered to the end user through access points. QlikView is offering the best when you want to create business applications for data analytics.

What are the features of QlikView?

·       QlikView is capable to run on both locally or on an on-premise server

·       Optionally QlikView is also a cloud solution

·       QlikView is famous for pixel perfect design

·       Promotes a dynamic BI ecosystem for better interactions

·       QlikView is known for easy creation of complex dashboards

·       Guided and advanced analytics

·       Capable of building guided analytics applications and dashboards

·       Presence of connecters that are both custom-made and default

QlikView Dashboard Example

What is Qlik Sense

 Qlik Sense is a powerful self-service data discovery and analysis tool focused on ease of use for the user. Qlik Sense gives the user complete control over access to pre-built charts, which means you can be a more active, engaged user.

Qlik Sense is introduced in the business intelligence tools market with updated technology which means users can make use of various user-friendly features like visualization and trendy data analysis. Qlik Sense has a simple drag-and-drop interface that allows for the creation of flexible and interactive visualizations that are displayed in a clear, orderly manner. Qlik Sense's user-friendly approach helps users quickly and easily create a visualization that tells a story without involving the design elements

What are the features of Qlik Sense?

 ·       Qlik Sense provides smart analytics visualization with drag and drop capabilities

·       Quick and reliable connections to multiple data sources

·       Fully web base with HTML and JavaScript supported

·       Mobile responsive supported in all screen mobile sizes with easy touch response. Available in iOS and Android apps

·       User-friendly and open API

·       Smart search options like Google and associative functions

·       Self-service data discovery

Qlik Sense Dashboard Example

What are the new capabilities in Qlik Sense?

Qlik Sense is a next-generation, self-service data visualization and analysis application that enables individuals, groups and organizations to rapidly create and share visualizations and dashboards without specialized development skills.

·       Qlik Sense does not require a 'build-and-publish' approach, as each user can simply drag-and-drop to build or enhance their visual analysis.

·       Qlik Sense allows users a smart search experience. Simply type numbers or words to start analysis from the entire data set.

·       For beginners, learners or trainees Qlik Sense provides smart visualization that helps to detect patterns with visualization by dynamically updating and highlighting new information and associations based on selection.

·       The data storytelling feature in Qlik Sense allows people to capture and share their insights one at a time in a presentation format.

·       Open Standard APIs support web app development for complex applications, allowing organizations to use Qlik Sense in new ways, among other technologies.

Difference Between QlikView and Qlik Sense

There are many similarities between QlikView and Qlik Sense, but each caters to particular markets and needs, with different purposes. In the below infographics, you can see a complete difference along with the features.

List of QlikView and Qlik Sense Common Capabilities

Centralized sharing and collaboration

The important operation in a situation where a team or group of users are involved in data analysis is sharing and collaboration. You can also use RESTful API connectors to connect and integrate with data sources such as LinkedIn, Facebook, Twitter, Adobe etc.

Mobility and Ease of Access

Mobile touch capabilities are making better and better with every new version of Qlik View and Qlik Sense. Thus, users get great mobility and ease in data analysis, report generation and sharing on the go. We can use Qlik View and Qlik Sense mobile capabilities on any device like a tablet, phone or laptop. Qlik View and Qlik Sense is compatible with platforms like iOS and Android. Thus, Qlik View and Qlik Sense can be used across all the mobile devices in its full capacity, loaded with all the features.

Security, Scalability, and Performance

Security, scalability and performance are the three important pillars of Qlik View and Qlik Sense's efficient functioning. It comes complete with centralized, rules-based security, multiple security layers, authentication security.

 Smart Visualization and Analytics

The Qlik product are very helpful in visualization as they are dynamic and smart. They are preloaded with varied types of charting options like pie, column/bar, line/area, scatter, plot, tree map, gauge, and combination charts.

Qlik Geo Analytics

This feature is a value-added service which can be used both in Qlik Sense and QlikView. The geo-analytics feature includes a geospatial analysis of location-based data by performing advanced geographic calculations on location and non-location data.

User-Driven Creation

There are many features which help the individual users to create their own application without essentially needing a technical hand. With Drag and Drop Feature You can load data files or create sheet objects by easily dragging the source item from the destination window and dropping in on the Qlik View and Qlik Sense application dashboard.

 Customization and Extension

Qlik View and Qlik Sense functionality can be developed, extended and embedded through an open API platform. Custom web applications can be developed, supported by the Qlik Engine. Web technologies like HTML5, JavaScript, and CSS3 are used, also, libraries like D3 and other JavaScript libraries are supported. While developing applications using JavaScript API, you can incorporate pre-build charts through code commands in the script and the associations between charts and other sheet objects occur automatically by the associative model of the Qlik Engine.

Associative Model

The associative model creates a relationship in the data which you can comprehend and use to draw better insights. The associations formed in the data answers a lot of questions regarding the hidden messages and patterns in the data.

 Will QlikView and Qlik Sense merge into one product?

Each product has different capabilities and is appropriate in particular situations.

They are separate products with their development roadmaps. QlikView is great for guided analytics while Qlik Sense is best for self-service visualisations.

Since they have different purposes, one isn’t intended to replace the other.

Because QlikView and Qlik Sense are based on the same core analytics engine, all future engine developments will benefit both product lines, and Qlik will continue investing in both platforms.

Why do people use QlikView and Qlik Sense?

If you haven’t used Qlik products before, here’s why users say they’re great:

They are easy to learn and simple to use

They open up a world of possibilities for making your data work     harder

They help make time-saving and accurate decisions and bring a new     level of insight to your data.

Advantage of using QlikView and Qlik Sense

QlikView Advantage

·      QlikView has a simple, relational data model that is really fast. Filtering and transforming data are dead simple, with results available almost immediately.

·       The free version of QlikView is almost fully featured, so you get a pro-level product rolling out across the department for really cheap.

·       QlikView is really flexible - if you can imagine it, you can build it.

Qlik Sense Advantage

·       Qlik Sense provides one of the most flexible data modelling experiences with load scripts and data manager.

·       Qlik Sense continues to iterate on the user interface and provides new charts and features regularly.

·       Qlik Sense delivers a true enterprise-level administrative experience, especially for security management.

Should I choose QlikView or Qlik Sense? Do I need both?

If you are new to Qlik and starting to use Qlik product then you should use Qlik Sense. Compared with QlikView, Qlik Sense provides users with a full range of capabilities for modern data analytics.

If you already have knowledge of QlikView, you can take it to the next level with Qlik Sense. With Qlik Sense, you can get the most out of self-service visualization, custom development, and embedded analytics.

While QlikView and Qlik Sense share the same engine, a lot of the work required to integrate and transform data from multiple sources can be shared between the two products. Many organizations use both QlikView and Qlik Sense because of their very different features and uses.

Conclusion:

From the above discussion we can conclude that both the tools offering a wide variety of capabilities. QlikView and Qlik Sense both users same Qlik engine which makes a powerful tool. Both the tools let to visualise a data with an ease and explore data. Business users select from this tool as per their requirements and developing potential. Both the tools help to view business insights and help business to make its future better. If your business is also generating huge data and need to view the business insights then you can register with EzInsights Free Trial Demo.