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Data governance efforts have been fragmented into specific areas of focus, such as data quality, master data management, or a data warehousing. However, organizations today need enterprise-wide data governance that often rely on all three. Thanks to dynamic data catalogs, enabled by data virtualization, enterprise-wide data governance is not only possible, but practically seamless. Originally designed  for data analysts, today data catalogs are instrumental to ensuring data governance and stewardship and more importantly to helping an organization understand their data assets so they can improve analytics business processes.

Breaking Down the Siloes that Impact Holistic Governance

While no one will debate the benefits of data governance, the challenge is that any technology that deals with data has some form of data governance already built into it. Master data management employs master data governance rules, specifying what constitutes master data, who owns that definition, who is allowed to make changes to it, and so on. Similarly, data warehouses are set up with rules governing which data can be stored for analysis, which BI applications and users can run which types of analysis and reports, and so on. However, these rules are imposed at the application or departmental level, but never at the enterprise level.

This creates many issues: First, business users who need to understand and use data across systems and applications are at a loss because they need to search for the required data in each of these systems separately and then manually relate them. Second, these systems have different levels of access security, so business users end up gaining only a partial view of the data they need. Finally, the documentation of the same data entities, such as customer, could be different in each of the systems, creating silos of similar information and ultimately conflicts.

The Case for Enterprise Data Governance

Business users always need to understand the ways that different data elements connect. A retailer might want to know which customers own which products, whether they bought them in the store or online, and if they have warranties for any of them. The relevant customer data could come from a CRM system, the product information from an ERP system, and the warranty information from a warranty registration system.

Only with a unified knowledge about the customer and his or her activities can a company be effective in pursuing revenue-yielding business initiatives such as cross-selling and up-selling. Business users, such as customer care representatives, would like to search for specific customers and see them in a 360-degree view that includes their purchased products, warranties, transactions, etc., to provide better customer service.

For such views, data governance would have to transcend local systems and rise to the enterprise level. Representatives would then be able to see all of the data across the relevant systems, the relationships among the data, and any associated notes. Data security would be enforced so that data can only be seen by people authorized to view it. For example, one global mobile insurance service provider was regulated for protecting Personally Identifiable Information (PII) in the countries it operated. As a result, the company enforced access controls that ensured that representatives providing services in certain countries could only see information relevant to those countries.

However, if each system – CRM, ERP, etc., – has its own product-specific data governance, how can an organization establish enterprise-wide data governance? One technology that is gaining attention for this broad use case is the dynamic data catalog made possible by data virtualization.

How Dynamic Data Catalogs and Data Virtualization Enable Enterprise-wide Data Governance

The data catalog builds upon the base views within the data virtualization layer by augmenting them with information about who owns the data, the history of the data, its lineage, the associations and relationships among the data, business definitions, secure access privileges, and much more. Since data virtualization is real-time data integration and delivery, the data catalog is updated in real time, hence the term dynamic data catalog.

So, how does a dynamic data catalog enable enterprise-wide data governance? First, it enables business users to perform data discovery using google-like search features to easily find data entities such as customers and products across the enterprise. Then they will be able to see the lineage of how this data has been combined with data from other systems. Also, they will be able to see the relationships of this data entity with others, such as which customers own which products.

Data owners can be empowered to document the business definitions for each of the entities, across all applicable data sources, simultaneously. They can package up their searches and resulting views into a query that can be invoked by other authorized users. Fine grained security can be used to protect any sensitive data by dynamically masking the fields for which certain users do not have the access privileges. In short, enterprise data governance is baked into the very architecture of the dynamic data catalog.

Creating a Future Proof Data Governance Capability 

Thanks to machine learning and data lakes, the proliferation of data is making it unbearable for organizations who struggle to access and understand it and for IT to properly manage. Enterprise-wide data availability has its merits but it also needs to be secured with the right level of access. Dynamic data catalogs, built on data virtualization, enable enterprise-wide data governance and ensures that all of the enterprise data is made available to business users with strict access controls. They augment real-time data views with business definitions, associations, lineage, and security. Because of these critical data governance capabilities, it is hard to imagine a  data management future without a dynamic data catalog.

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From Big Data To Smart Data

Fight the Big Data Backlash and use Smart Data help you identify purchase intent

Big data is starting to experience some significant backlash. A ‘case in point’ comes from a recent popular article in VentureBeat: ‘Big data’ is dead. What’s next? The backlash is more to do with the buzz than the data but the reason relates to the difficulty of extracting meaningful insights from big data.

Born from the backlash comes another buzzword; smart data, a means of extracting these meaningful insights from big data.

Looking past the marketing hype, smart data is actually the metamorphosis of big data into something actionable. Here we look at recognizing purchase intent as an example of actionable data extraction.

Big data vs Smart data rundown Big data, strong signals, Smart Insights

The big opportunity for big data is how to extract a ‘strong signal’ from the noise. Collecting big data and mining it mercilessly is not the opportunity. The opportunity is leveraging ‘a strong signal’ data set and integrating it to label big data, thus making it immediately usable. This is where an information rich contextual data set can inform big data and turn it into smart data.

Let’s take a real example: Say you were trying to identify and target website visitors who intend to purchase. If you were to rely only on mining your web analytics data for this information you would have to sort through the entire data set looking for the behavioral traits of purchase intenders. This not only is difficult but could be wildly inaccurate. You would think that focusing on the shopping cart is all you would have to do to get a stronger signal of purchase intent, but there is more to the story. Data shows that for a typical e-commerce site only 44% of visitors that enter the cart actually have the intent to purchase while the remaining 56% represent all other intent types such as researchers.

By labeling your data set with a ‘strong signal’ such as visitors who are actually intending to purchase, you can segment and contextualize the web data illuminating the most important aspects of the data set.

Empowering your Big Data

Collecting visitor stated intent, or in other words the way someone describes their intention for visiting a website, provides a much stronger signal because it is the visitor who describes their intention.

iPerceptions research shows that a visitor who states that they intend to ‘purchase’ is 15 to 20 times more likely to do so than someone who describes their intent to ‘research’.  This powerful qualitative intent data paired with quantitative and descriptive data creates contextualized data sets, transforming your big data into smart data.

Putting it all together – Big and Smart Data

Big data is complex and vast but many of the benefits cannot be truly realized without adding contextual information. If these data sources are combined not only can you transform big data into smart data, but you can also provide enormous windfalls for consumers and companies alike improving the customer experience and the company’s ability to meet the needs of its customers. However having the right type of data is only half the story. To make personalization a reality and directly impact the customer experience, a real-time approach to leveraging this information must be taken so that the quickly eroding opportunities can be recognized and acted upon.

Building An Innovative Culture To Thrive With Data

“It is a capital mistake to theorize before one has data,” said Sherlock Holmes. The words of literature’s most famous detective ring true nearly 100 years later. Organizations today should take note, as the potential for data-driven business strategies and information products is greater than ever. “The goal is to build a data-driven organization,” says Mike Rollings, research vice president at Gartner. “And although digital business thrives on

Data-driven culture starts at the (very) top

Companies with strong data-driven cultures tend have top managers who set an expectation that decisions must be anchored in data — that this is normal, not novel or exceptional. They lead through example. At one retail bank, C-suite leaders together sift through the evidence from controlled market trials to decide on product launches. At a leading tech firm, senior executives spend 30 minutes at the start of meetings reading detailed summaries of proposals and their supporting facts, so that they can take evidence-based actions. These practices propagate downwards, as employees who want to be taken seriously have to communicate with senior leaders on their terms and in their language. The example set by a few at the top can catalyze substantial shifts in company-wide norms.  

Choose metrics with care — and cunning

Leaders can exert a powerful effect on behavior by artfully choosing what to measure and what metrics they expect employees to use. Suppose a company can profit by anticipating competitors’ price moves. Well, there’s a metric for that: predictive accuracy through time. So a team should continuously make explicit predictions about the magnitude and direction of such moves. It should also track the quality of those predictions – they will steadily improve!  

Don’t pigeonhole your data scientists

Data scientists are often sequestered within a company, with the result that they and business leaders know too little about each another. Analytics can’t survive or provide value if it operates separately from the rest of a business. Those who have addressed this challenge successfully have generally done so in two ways.  

Fix basic data-access issues quickly

By far the most common complaint we hear is that people in different parts of a business struggle to obtain even the most basic data. Curiously, this situation persists despite a spate of efforts to democratize access to data within corporations. Starved of information, analysts don’t do a great deal of analysis, and it’s impossible for a data-driven culture to take root, let alone flourish. Top firms use a simple strategy to break this logjam. Instead of grand — but slow — programs to reorganize all their data, they grant universal access to just a few key measures at a time.  

Quantify uncertainty

Everyone accepts that absolute certainty is impossible. Yet most managers continue to ask their teams for answers without a corresponding measure of confidence. They’re missing a trick. Requiring teams to be explicit and quantitative about their levels of uncertainty has three, powerful effects.  

Make proofs of concept simple and robust, not fancy and brittle

In analytics, promising ideas greatly outnumber practical ones. Often, it’s not until firms try to put proofs of concept into production that the difference becomes clear. One large insurer held an internal hackathon and crowned its winner — an elegant improvement of an online process — only to scrap the idea because it seemed to require costly changes to underlying systems. Snuffing out good ideas in this way can be demoralizing for organizations. A better approach is to engineer proofs of concept where a core part of the concept is its viability in production. One good way is to start to build something that is industrial grade but trivially simple, and later ratchet up the level of sophistication.  

Specialized training should be offered just in time

Many companies invest in “big bang” training efforts, only for employees to rapidly forget what they’ve learned if they haven’t put it to use right away. So while basic skills, such as coding, should be part of fundamental training, it is more effective to train staff in specialized analytical concepts and tooling just before these are needed — say, for a proof of concept. One retailer waited until shortly before a first market trial before it trained its support analysts in the finer points of experimental design. The knowledge stuck, and once-foreign concepts, such as statistical confidence, are now part of the analysts’ vernacular.  

Use analytics to help employees, not just customers Be willing to trade flexibility for consistency — at least in the short term

Many companies that depend on data harbor different “data tribes.” Each may have its own preferred sources of information, bespoke metrics, and favorite programming languages. Across an organization, this can be a disaster. Companies can waste countless hours trying to reconcile subtly different versions of a metric that should be universal. Inconsistencies in how modelers do their work takes a toll too. If coding standards and languages vary across a business, every move by analytical talent entails retraining, making it hard for them to circulate. It can also be prohibitively cumbersome to share ideas internally if they always require translation. Companies should instead pick canonical metrics and programming languages. One leading global bank did this, by insisting that its new hires in investment banking and asset management knew how to code in Python.  

Get in the habit of explaining analytical choices

Drive Business Success With Data Science Corporate Training


Data science has become an essential element for success in today’s fast-paced business environment. The demand for people with data science skills is increasing quickly, with an estimated 2.7 million new positions in the data industry anticipated to be generated by 2023. Corporate training in employee development and upskilling is now more important than ever as businesses depend more and more on data-driven decision-making to remain competitive. In fact, it is anticipated that the global market for data science training will reach $13.8 billion by 2026, demonstrating the enormous importance that businesses attach to this area.

Table of Contents The Growing Need for Data Science Skills

Businesses across industries are utilizing data science approaches to extract worthwhile insights and make better decisions due to the emergence of big data. According to estimates, businesses using data-driven insights steal $1.8 trillion from their less informed rivals yearly. Data science expertise is becoming a requirement for businesses looking to keep their competitive edge rather than a luxury.

A team with good data science skills can considerably increase a company’s productivity, decision-making skills, and innovation potential. Data scientists can analyze large datasets to find trends and patterns, allowing organizations to make data-driven choices, streamline operations, and create new goods and services. Companies are, therefore, actively working to upskill their staff and foster a more data-driven workforce. As a matter of fact, 87% of businesses feel that having strong data science and analytics abilities is key to their performance, making corporate training programs more and more critical.

In-House vs. External Data Science Corporate Training

The two main possibilities for data science training within organizations are internal and external. Both strategies have benefits and drawbacks, so it’s critical to pick the technique that best suits the requirements and objectives of your firm.

In contrast, external training entails collaborating with reputable organizations providing in-depth data science courses and programs. Employees can gain knowledge from experienced industry professionals by choosing this alternative, which may be more affordable. However, external training could not be as adaptable as internal programs and might not adequately address the particular demands of your business.

Consider aspects like your budget, the level of customization required, and the competence of your in-house staff when deciding on the appropriate training approach for your company.

Top Corporate Training Programs for Data Science

Numerous corporate training programs for data science are available, ranging from online courses and workshops to intensive boot camps. To help you make an informed decision, we have curated a list of top training options:

Analytics Vidhya:  An extensive Corporate Training program is available from Analytics Vidhya, a top source of data science training, to give your staff the most recent data science capabilities. The program is designed to address the unique demands of your organization and includes important topics like data visualization, machine learning, and artificial intelligence. Businesses looking to upskill their staff in data science will find Analytics Vidhya to be a great option because of its knowledgeable teachers and successful track record.

DataCamp:  Business workers can choose from a sizable selection of online data science courses offered by DataCamp. They ensure that staff receives hands-on experience with data science tools and methodologies through their corporate training program, which consists of interactive classes, practical exercises, and real-world case studies. Additionally, DataCamp offers talent evaluations and progress tracking, allowing businesses to analyze staff development and pinpoint areas for development.

General Assembly:  A well-known supplier of data science boot camps and workshops is General Assembly. Their corporate training programs are developed to give staff in-depth, practical learning opportunities. The data science courses offered by General Assembly include machine learning, data visualization, and predictive analytics. This immersive method is excellent for organizations wishing to upskill their workers and provide noticeable results quickly.

Benefits of Corporate Training for Data Science

Increased Productivity:  A skilled staff may use data science techniques to improve decision-making, streamline procedures, and spur innovation. This boosts production and efficiency, which helps the bottom line of your business.

Employee Retention: Offering opportunities for professional development to employees can boost job satisfaction and lower turnover rates. The fact that you spend money on data science training shows how much you care about your employees’ professional development and how highly you regard their knowledge and abilities.

Adapting to New Technologies: With new tools and methodologies being developed constantly, data science is a rapidly growing topic. You can ensure that your business can adapt to these changes and maintain its position at the forefront of industry innovation by giving your personnel the most recent corporate training.

Case Study: Enabling India’s Automobile Giant to Upsell and Cross-Sell Using Data Analytics

Client:  One of India’s largest conglomerates, the client has over 50 enterprises under its wing. The client group’s flagship company is a top maker of two- and three-wheeled vehicles and is India’s third-largest motorcycle manufacturer.

Objective:  The client wanted to find new customers so they could upsell and cross-sell their vehicle products to them. They intended to use data analytics, machine learning, data engineering, and cloud platforms like AWS to do this.

Challenges: Included a dearth of data-driven insights, laborious manual processes, and trouble determining the appropriate customer groups. As a result, the business could not fully utilize its enormous consumer database.

The Solution Offered by Analytics Vidhya: In close collaboration with the stakeholders, Analytics Vidhya created a solution that was specifically tailored to meet their needs. The corporate training program was thoughtfully designed to equip more than 250 students with the abilities and information required to successfully utilize data analytics, machine learning, and cloud platforms.

Customized Corporate Training Program for the Client:  A thorough training course that covered a wide range of subjects, including data analytics, machine learning, data engineering, and cloud computing services like AWS.

Application-Based Learning:  Case studies, practical exercises, and examples from everyday life were all included in the curriculum to help students understand how to use these technologies in their jobs.

High Impact Outcome:  The students used their newly acquired knowledge to locate potential clients for upselling and cross-selling, significantly increasing sales and earnings for the business. This success story exemplifies the transformative potential of corporate data science training in generating measurable business results.

Analytics Vidhya has successfully trained over 3,500 learners across various geographies and industries through bespoke training programs customized and tailored for our clients. Transform your data science and analytics team with Analytics Vidhya’s customized training solutions.  Learn more about Analytics Vidhya’s Enterprise training here.


In conclusion, data science expertise is crucial for modern firms that want to survive and prosper in today’s data-driven economy. Corporate training is necessary for companies wishing to equip their workers with these vital skills. You may choose the finest training solution for your organization by researching the training programs discussed in this article and speaking with professionals.

Are you prepared to equip your team with fundamental data science abilities? With the help of Analytics Vidhya’s corporate training programs, start your company down the path to a more data-driven future. With customized content and qualified instruction, your staff will be well-equipped to drive innovation and success in the big data era.


10 Best Data Analytics Projects With Source Codes


Not a single day passes without us getting to hear the word “data.” It is almost as if our lives revolve around it. Don’t they? With something so profound in daily life, there should be an entire domain handling and utilizing it. This is precisely what happens in data analytics. People equipped with the technical know-how spend hours on end muddling with datasets. But how do you get there? It may seem an intimidating area, but it is rather intriguing. All you need is a basic understanding of data technologies work, experience working on data analytics projects, and an eye for detail.

Irrespective of your place in the data journey, data analytics projects add significant value to your expertise, resume, and the real world. This article enlists and discusses the 10 best data analytics projects.

Let’s get started with a few fundamental concepts first.

Types of Data Analytics Projects

There are four primary types of data analytics projects: descriptive, diagnostic, predictive, and prescriptive. Each type has its own goals and objectives. Read on to learn more about each explicitly.

Descriptive Analytics Projects

Descriptive analytics is one of the most widely used types of analytics, primarily because it conveys “what is there and what has happened.” Consequently, descriptive projects focus on using historical data and getting an understanding of trends/patterns for future use. The main goal is to gain insights into trends and patterns to help inform future decisions.

Descriptive analytics projects may include the following.

Social media analytics for platforms like Instagram.

Marketing campaigns’ performance analysis to study sales patterns.

Stock market analysis.

Diagnostic Analytics Projects

As the name suggests, diagnostic analytics refers to identifying a problem and then seeking its root causes. As a result, the projects involve analyzing data to understand why something happened and what factors contributed to it.

One of the most standard applications of diagnostic analytics is in the cybersecurity domain. Cybersecurity specialists utilize the same to study data breaches and find a connection between them and security ratings.


Examining Market Demand

Improving Company Culture

Identifying Technology Issues

Predictive Analytics Projects

The subsequent step to any descriptive analytics task involves predictive analytics. The latter is all about using statistical methods and machine learning models to predict future states. Consequently, predictive analytics projects aim to use these predictions to make more informed decisions and optimize business processes.

Such projects often involve:

Root-cause analysis: to think “why?” (implying that predictive projects also involve diagnostic analytics).

Data mining: to find any possible correlations between data from different sources.

Sentiment analysis: to determine the sentiment associated with the text.

Prescriptive Analytics Projects

Prescriptive analytics combines predictive analytics with several optimization techniques to recommend or “prescribe” specific tasks or remedies. These projects aim to optimize and improve business processes, resource allocation, and strategic decision-making.

These tasks are tailored to achieve the desired outcome. Prescriptive analytics is widely used for resource allocation, designing personalized marketing campaigns, energy grid management, and a lot more.

Steps Involved in Data Analytics Projects

1. Customer Segmentation Analysis

Imagine pitching premium products to a customer who shops economically or offering bundled products to someone who prefers a single yet priced product. Will this convert?

Probably not. None of the policies checks out the one-size-fits-all criterion, as customers have unique needs and expectations. This is where customer segmentation analysis can save a lot of time and ensure maximum results.

A customer segmentation project aims for data analysts to identify different groups of customers with similar needs and behaviors so that companies can tailor their marketing, product development, and customer service strategies to meet their needs better. This can be done by clubbing them as per: marital status, new customers, repeat customers, etc.

Luxury car manufacturers like Rolls Royce often use lifestyle-centric segmentation analysis to segment their top customers. Clearly, a data analyst familiar with customer segmentation would be a great asset to such businesses.


You can find the source code for customer segmentation analysis projects here.

2. Sales Forecasting Analysis

Estimating future sales, or revenue for that matter, is a pronounced and essential business practice. As per Hubspot’s research, more than 85% of B2B companies use such data analytics, making sales forecasting projects well-decorated project ideas for analysts.

These projects estimate the revenue the company expects to earn over a pre-decided period, usually 1 year. This amount is computed using several factors, including previous sales data, market prices, demand, etc. As sales forecasting is an ongoing process, the work involves constant updates and bug fixes. Working as a sales forecasting data analyst would be a great option if you are proficient and prompt with constantly running data pipelines.

Companies like BigMart, Amazon and Flipkart rely heavily on sales and revenue forecasting to manage inventory and plan production and pricing strategies. This is primarily done during peak shopping seasons like Black Friday or Cyber Monday.


You can find sales forecasting analysis source code here.

3. Churn Prediction Analysis

Customer behavior is still a mystery for all. More often than not, businesses need to predict whether customers will likely cancel their subscription or drop a service, also known as “churn.” Churn prediction analysis aims to identify customers at risk of churning so companies can proactively retain them.

A data analytics project based on predicting customer churn has to be highly accurate, as many people, including customer success experts and marketers, depend on the project findings. This is why data analysts work with high-performing Python libraries like PyPark’s MLIB and some platforms and tools like Churnly.


You can find churn prediction analysis source code here.

4. Fraud Detection Analysis

The next on our list of analytics projects deals with fraud detection. Fraud detection analysis aims to prevent financial losses and protect businesses and customers from fraud. This is done using several KPIs (key performance indicators) mentioned below.

Fraud Rate.

Incoming Pressure (the percentage of attempted transactions that are fraudulent).

Final Approval Rate.

Good User Approval Rate.

Data analysts are expected to calculate these metrics using historical customer and financial data and help companies detect fraud. One example of a company hiring data analysts for fraud detection is PayPal. PayPal uses manual review processes to investigate suspicious transactions and verify user identities.


You can fin fraud detection analysis source code here.

5. Social Media Sentiment Analysis

Sheerly, because of the vast number of people using social media to voice their opinions and concerns, it has become increasingly vital to analyze the sentiment behind it. Many companies undertake sentiment analysis to ensure these platforms are safe and sound for society.

Working on real-life big data projects as a learning data analyst gives an idea of how the knowledge is relevant and applicable to the real world. Moreover, social media is transforming into a highly sought-after area of work as social media giants like Facebook, Instagram, etc., are rapidly hiring professionals to analyze sentiments.


You can find social media sentiment analysis source code here.

6. Website User Behavior Analysis

Analyzing how users behave and interact with a product/service on your website is vital to its success. Once you understand their behaviour more deeply, you can discover more pain points and tailor a better-performing customer experience. In fact, 56% of customers only return if they have a good experience.

To ensure everything sails smoothly on a website, data analytics projects involve visualizations (using heatmaps, graphs, etc.) and statistical analysis of user survey data. You will use Python libraries like matplotlib, seaborn, and NumPy, R libraries like ggplot2, dplyr, etc., to map proper user behavior.

Tech companies like Google and Microsoft and medical research companies like Mayo Clinic hire data analysts to work, especially on user behavior analysis.


Here is the source code for website user behavior analysis.

7. Inventory Optimization Analysis

The process can also involve forecasting demand for each product, analyzing inventory turnover rates, and identifying slow-moving or obsolete products. You will be:

Finding target personas,

Studying purchasing (or sales) patterns,

Identifying key locations and seasonal trends,

And optimizing the inventory size.

With experience in inventory analysis, you can seek professional opportunities in e-commerce companies like Amazon, Myntra, Nykaa, etc.


You can find the source code for inventory optimization analysis.

8. Employee Performance Analysis

As the name suggests, employee performance analysis is a process of analyzing employee data to identify patterns and trends that can help improve employee productivity, engagement, and retention. It can be an excellent practice area as you will deal with data containing different data types, like numerical (attendance, turnover rates, etc.) and categorical (job satisfaction, feedback, etc.).

In such a project, you will need to:

Set goals and decide on performance metrics,

Collect feedback data,

Use this data for preprocessing and analysis,

Infer who performs the best.

You can also work with visualization tools like PowerBI and create dashboards for each department. Or you take up a proper data analytics workflow and do exploratory analysis using Python’s Pandas, NumPy, matplotlib, and Seaborn. Getting good at this analysis will open doors for a promising career in almost any field.


You can checkout the source code for employee performance analysis here.

9. Product Recommendation Analysis

This is one of the most common data analytics projects. It involves collecting and analyzing data on customer behavior, such as purchase history, browsing history, product ratings, and reviews. The practice is so common that the recommendation engine market is bound to reach over $15,13B by 2026!

It is widely used by e-commerce websites that believe a product display influences shoppers’ behaviour. It has been researched that over 71% of e-commerce websites now offer recommendations after a comprehensive review of historical website data. Analysts spend days and weeks visualizing sales, purchases, and browsing histories using Python libraries like Seaborn, matplotlib, etc.

Proficiency in this data analytics segment can help you build a promising career in companies like YouTube, Netflix, and Amazon.

Project Pro

You can checkout source code for product recommendation analysis here.

10. Supply Chain Management Analysis

Supply chain management involves the planning, execution, and monitoring of the movement of goods and services from suppliers to customers. Following the same, a data analytics project on supply chain management requires you to work on the following:

Demand forecasting,

Inventory management,

Analysis of supplier performance,

Logistics optimization, etc.

The main idea is to study all the factors and see how each one of them affects the chain. Many companies are indulging in supply chain analysis. For example, PepsiCo utilizes predictive analytics to manage its supply chains. As a result, the company actively hires seasoned data analysts familiar with supply chain management. The main idea is to study all the factors and see how each one of them affects the chain.

Network Computing

You can check the source code for supply chain analytics here.

Best Practices for Successful Data Analytics Projects 1. Data Quality and Integrity

A data analytics expert works with vast volumes of data during the entire process of collecting data, preprocessing it, and finally using it for analysis and interpretation. This makes it vital for them to prioritize some of the steps that ensure data cleaning and manipulation is done ethically. While they are free to wrangle data in any form demanded by the project, they must retain all the information, keeping the quality and completeness intact as it directly impacts the accuracy of results.

2. Collaboration Between Teams

Fostering an environment of collaboration and alignment among the team members and different teams sets the project on a successful track. This is because different teams, and individuals, bring different skills and perspectives to the table, resulting in a more diverse and complete analysis.

3. Communicating Results Effectively

Communication is key. It is not only a mantra to success but something that keeps everyone on the same page. Good communication ensures that each team member knows the project’s goals and expectations and can pass on the project findings to all technical and non-technical stakeholders.

4. Continuous Learning and Improvement

Data analytics is an iterative process, and there is always room for improvement. Continuous learning and improvement ensure that the data analytics project results are credible and all necessary changes to improve the accuracy and relevance of the insights are taken into account.

Programming Languages (Python, R)

Python and R are the most popular programming languages in data analytics projects. Both languages offer a wide range of tools and technologies for the same.

Python is a general-purpose programming language. It comes with a bunch of libraries and frameworks like matplotlib, scikit-learn, TensorFlow, pandas, numpy, statsmodel, and many more. These components are widely used in exploratory programming, numerical computation, and visualization.

R programming is a language specifically designed for data analysis and statistical computing. It offers numerous tools and technologies like dplyr, ggplot2, esquisse, BioConductor, shiny, lubridate, and many more.

If you do not wish to avoid getting your hands dirty during the data analysis process, you can work with some visualization tools. As you are probably working through the data domain, you must be aware of Tableau and Power BI.

Data blending,

Interactive dashboards,

Drag-and-drop interfaces,

Data Mapper, etc.


On the other hand, Power BI is a business analytics service by Microsoft that works similarly and helps in data visualization. However, it is a bit more sophisticated than Tableau and hence, has a steeper learning curve. Power BI offers:

Natural language querying,

Interactive dashboards,

Data modeling, etc.

K21 Academy

Big Data Technologies (Hadoop, Spark)

Big data technologies like Hadoop and Spark are widely used for data analytics projects, especially when organizations need to process and analyze big data.

Hadoop is an open-source software framework that enables distributed processing of large data sets across clusters of computers. Hadoop offers:

Hadoop Distributed File System (HDFS),

YARN (for resource management),

MapReduce, etc.


Spark, on the other hand, is an open-source, distributed computing system that is designed for processing large-scale data sets. Surprisingly, Spark is built on top of Hadoop. Data analysis tools and techniques that Sparks offers:

Spark SQL (for data processing SQL queries),


Spark Streaming, etc.

Crossroad Elf

Importance of SQL in Data Science Projects

If you’re not familiar with how to store structured data, manage its access, and retrieve it when required, you’ll have a hard time working as a data analyst or scientist. SQL is the most famous programming language for storing structured data in relational databases (containing data in a tabular format). As data science is a field brimming with tonnes of data, SQL comes in handy in the manoeuvring of data and storing operations.

In fact, many job positions require analysts to be proficient with SQL querying and manipulation. Moreover, several big data tools like Hadoop and Spark offer explicitly designed extensions for SQL querying just because of how extensive their usage is.


You must now know the vitality of data analytics projects. While they are vital, driving an entire project to success can be challenging. If you need expert guidance to solve Data Science/Analytics Projects, you’ve landed at the right destination. Analytics Vidhya (AV) is a career and technology-focused platform that prepares you for a promising future in data science and analytics while integrating modern-day technologies like machine learning and artificial intelligence. At AV, we realize the importance of staying up to date with recent technologies and hence, offer comprehensive courses. To fuel your career in the domain, we provide a Blackbelt Program in AI and ML, with one-on-one mentorship. Enrol and witness the best learning experience and interview guidance.

Frequently Asked Questions

Q1. Do you need programming skills to do data analytics projects?

A. Having programming skills can be helpful for data analytics projects, but it’s not always necessary. There are tools like Tableau and Excel that allow you to analyze data without coding.

Q2. What are some popular tools for data analytics?

A. Some prominently used data analytics tools used are Python, R, SQL, Excel, and Tableau.

Q3. What are some good data analytics projects for the intermediate level?

A. Some good data analytics projects for the intermediate level include predicting stock prices, analyzing customer churn, and building a recommendation system.



Anychart: Turning Data Into Actionable Insights With Award

Founded in 2003,

Emerging Out of Challenges

The company’s story began more than 16 years ago. At that time, AnyChart’s founders saw a big opportunity in the growing adoption of Flash and XML. They understood it was possible to extract data from literally any source in the XML format and then visualize it as an interactive chart in Flash. Flash was everywhere, so AnyChart succeeded as a “one-size-fits-all” tool for interactive charting. At the time of AnyChart’s inception, Flash and XML were still young technologies. That resulted in a lot of bug reports as well as security and related issues in Flash, causing problems that did not depend on the team. When big companies like Oracle started joining the company’s customer list, the company began to deal with a really massive flow of queries and reports from thousands of developers from all over the world. To cope with that, AnyChart had to entirely rethink and rearrange its technical support and product development activities. That optimization was one of the key elements of its success, and its current level of customer support and work with customers in general is known to be especially high. Moving ahead, as Flash was falling down and HTML5 was growing as the modern web’s core standard, AnyChart reduced its dependency on Flash by adding the support of SVG and releasing its own JavaScript charting library as a brand new product, from scratch. Then the company completely shifted to HTML5 while still supporting its Flash components for the customers who keeps using that technology. Yet one of the biggest challenges in the life of AnyChart — that’s how the team remember that transition.  

Current Leader of Data Visualization Tools

Currently, the core product of AnyChart is a powerful feature-rich JavaScript charting library. It seamlessly works with all major programming languages, frameworks, libraries, and databases. Developers enjoy using it to create custom data visualizations, natively embedding those charts into various websites, corporate reporting, analytics and BI applications, mobile projects, OEMs, and SaaS products. AnyChart is proud to serve more than 70% of the Fortune 1000 members and more than half of the top 1000 software vendors worldwide. Thousands of businesses around the globe use AnyChart to power their data analytics and business intelligence, from startups to the biggest corporations including but far from limited to Microsoft, Samsung, Oracle, Volkswagen, Rolex, Bosch, McDonald’s, Lockheed Martin, Reuters, Citi, and many others. The company has customers from a wide range of industries: health care, media, retail, telecom, oil, software, and so on. Featured in numerous lists and directories as one of the best JavaScript charting libraries, AnyChart has also won multiple awards. For instance, the jury — comprising experts from Google, IBM, TIBCO, Mozilla, and other top technology companies — named AnyChart the best innovation in JavaScript Technology at last year’s DEVIES Awards within the framework of the world’s largest regular developer tech event, DeveloperWeek in California.  

Flexible Creative Solution

AnyChart is a highly customizable data visualization tool. Graphics are rendered using GraphicsJS — it is the company’s own innovative open-source JavaScript library for drawing SVG graphics (with VML fallback in older browsers), which allows developers to modify charts in endless ways. Although technically AnyChart is one big library, for customer and development convenience, it is marketed as a product family that consists of four JavaScript charting libraries, each optimized to serve a specific data visualization purpose: • AnyChart — for making basic JS charts of more than 70 chart types. • AnyGantt — for Gantt charts, both project and resource, and PERT charts. • AnyMap — for maps and seat charts. • AnyStock — for stock, financial, or any date/time charts. This year has been particularly exciting for the company. In spring of 2023, AnyChart announced a technology partnership with Qlik, adding three extensions for Qlik Sense with a fourth on the way soon. By integrating seamlessly into the Qlik environment and bringing more than 30 chart types (and counting), AnyChart has opened up multiple new charts to the Qlik community and engaged with an entirely new set of users in addition to the previously earned customer base.  

Valuable Testimonials

“AnyChart technology has allowed us to significantly improve Oracle Application Express’s charting capabilities.” — Michael Hichwa, Vice President of Software Development, Oracle. “AnyChart’s powerful logarithmic charting allowed us to display complex acoustical data in an easy to understand interface.” — Erik Gundersen, Director Advanced Products, Rockford Corp. “AnyChart generates all our charts in our Health and Wellbeing Profiles. To have so many options has been brilliant! The range of charts available and the amount of customization is impressive. Also, the ability to add error bars was essential for us.” — Mark Painter, Community Intelligence Team, UK’s Devon County Council.  

AnyChart Never Stops

Already at the top position in the segment of data visualization software for business intelligence and analytics, AnyChart is aimed at further strengthening the company’s global leadership.

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