Programming helps you apply statistics to data: Data analysts are well-versed knowledge of analytics tools, but without adequate coding proficiency, they can’t apply analytical techniques to data. Programming provides the tools to apply statistics to data sets. With programming languages like R or Python, analysts can customize these techniques to the data at hand.

A Primer on Data Analytics and Programming

Data analytics involves examining, cleaning, transforming, and modeling data to extract useful information, draw conclusions, and support decision-making. On the other hand, programming is the backbone that enables efficient data analytics. Languages like Python, R, and SQL are popular choices in the data analytics realm. Understanding these languages and their libraries and tools is essential for anyone aspiring to become proficient in data analytics. Programming assignment help aids in this quest by providing support and guidance in coding, which is fundamental to data analytics.

Data analytics tools such as SAS, SPSS, and Excel allow for further analysis and presentation, including interactive data visualizations. Understanding the functionalities of these tools makes it simpler to interpret the data and draw meaningful conclusions. Although the need for programming may be reduced in these cases, an understanding of the fundamentals goes a long way in data analytics.

Streamlining Data Cleaning and Preprocessing

One of the critical steps in data analytics is data cleaning and preprocessing. It includes dealing with missing values, outliers, and normalization. Programming assignment help can be invaluable in understanding the algorithms and codes used for data cleaning. With tutorials, examples, and coding assignments, learners can familiarize themselves with libraries like Pandas in Python, which are instrumental in data cleaning. Mastery of data cleaning codes through programming assignment help ensures that the data is reliable and ready for analysis.

Enhancing Analytical Skills Through Data Visualization

Data visualization is an essential component of data analytics. It enables analysts to see patterns, trends, and insights that might not be evident in raw data. Programming languages offer various libraries for data visualization, such as Matplotlib for Python and ggplot2 for R. Programming assignment help can guide learners through the creation of plots, charts, and dashboards, ensuring they can effectively communicate the insights gleaned from data. By practicing through assignments, learners can enhance their skills and understand the best ways to represent data visually.

Mastering Statistical Analysis Techniques

At the core of data analytics is statistical analysis. To discern patterns and make predictions, analysts employ a range of statistical techniques such as regression analysis, hypothesis testing, and clustering. Programming assignment help can aid in mastering these techniques by providing coding assignments focusing on the application of statistics in data analytics. With guided assistance, learners can grasp the intricacies of writing codes for statistical analysis and implement them to uncover insights in datasets.

Data analysis also involves the use of visual tools. Charts and graphs provide a visual representation of data, enabling analysts to quickly uncover trends and draw insights from them. Programming assignment help can provide guidance on the coding needed to create and customize various types of charts and visualizations. They can also support individuals in developing the skills necessary to identify effective visual representations of data and effectively convey insights with graphically presented data.

Encouraging Exploration and Innovation

Programming assignment help is not just about solving problems; it’s also about fostering a sense of exploration and innovation. As data analytics is an evolving field, being curious and innovative is paramount. Programming assignment help can offer challenging assignments that push learners to think outside the box. By tackling these challenges, they can explore new ways of analyzing data, applying algorithms, or creating visualizations. This encouragement to innovate is essential for becoming a proficient data analyst capable of solving complex analytical problems.

Data analytics is about transforming data into information, information into knowledge, and knowledge into wisdom. Programming is the vehicle that drives this transformation. With programming assignment help, aspiring data analysts can streamline data cleaning, enhance data visualization skills, master statistical analysis techniques, and foster innovation. The amalgamation of programming startup.info proficiency and analytical prowess can unlock insights that are indispensable in today’s data-driven world. Investing in programming assignment help is an investment in building a foundation for a rewarding career in data analytics.

The post Data Analytics: Unlocking Insights with Programming Assignment Help appeared first on Da-Research PR.

The first analytics toolkits were based on semantic models from business intelligence software. They helped ensure effective management, data analysis, and consistency across tools. One drawback was the unavailability of timely reports. Business decision makers were not always confident that the results matched their original request. Technically, these models are used mostly locally, making them cost inefficient. In addition, the data often ended up isolated in disparate repositories.

Subsequently, an evolution in self-service tools made data analytics available to a wider audience. These tools promoted the proliferation of data analytics because they did not require special skills to operate. Desktop business analytics has gained popularity over the past few years, especially when working in the cloud. Business users are enthusiastically exploring a wide variety of data assets. The ease of use is appealing, while combining data from different sources and creating a “single version of reliable data” is becoming increasingly challenging. Desktop data analytics can’t always scale for use across large groups. There is also the risk of inconsistent definitions.

More recently, analytics tools are enabling broader transformation of business insights by automatically updating and automating data discovery, cleansing and publishing processes. Business users can work on any device with context, get real-time information, and achieve results.

Today, most of the work is still done by humans, but automation is gaining traction. Data from existing sources can easily be combined. The consumer performs queries, then analyzes the results, interacting with visual representations of the data, and creates models to predict future trends or conclusions. All of this happens under the direction and control of humans at a deep granular level. Incorporating data collection, data discovery, and machine learning provides the end user with more options and happens faster than before.

The post In the future: next generation analytics appeared first on Da-Research PR.

Here are three situations where businesses can benefit from a big data analyst:

Incomplete purchases. In an online store, users add items to the cart, but then leave the site without placing an order. A big data analyst first finds out at what point a user loses interest. For example, leaving the site when they see a complicated registration form. Then suggests and tests hypotheses to help retain the customer and drive the store to the desired outcome (checkout).

“Bad” debts. The bank wants to minimize the number of customers who don’t pay back loans. The analyst looks at what characteristics of the customer indicate whether they will make their payments on time. On this basis, the customer will be approved or not approved for a loan.

Checking the effectiveness of the design solution. The creators of a dating app want to understand how users react to the color of the button. A data analyst will test two prototypes: one part of users see a version with a blue button, and another part sees a version with a red button. In the end, it helps the interface designer decide which color the button will work best.

Qualitative data analysis can also:

• identify present and future customer needs;
• predict the demand for a product or service;
• estimate the probability of error in different actions;
• control the operation and wear and tear of equipment;
• manage logistics;
• Monitor the efficiency of employees.

All this helps the company learn more about itself, increase profits and reduce costs.

What knowledge and skills does a data analyst need?
Here’s a starter pack for the novice data analyst:

• Work with data using Google Sheets, Sublime, Excel;
• use at least one programming language to solve problems: Python or R;
• Write queries to SQL databases;
• implement reporting in BI-systems: Tableau, Power BI, Google Data Studio, etc;
• Have basic knowledge of statistics.

Depending on the area, specific tools can be added.

The post What companies need data analysts? appeared first on Da-Research PR.

A data analyst helps a business not get lost in an ocean of information – structuring and interpreting it to present it in a clear format.

A data analyst is a big data analyst: he collects it, processes it, and draws conclusions. Based on his reports, companies make important decisions.

For example, in online commerce, you can analyze how customers use promo codes and what content is of most interest to website visitors, and based on this, decide which sites to use for promotion. In large chain stores, based on analysts’ conclusions, they optimize logistics and work with the flow of customers.

Almost all large companies are engaged in the collection of data. They are needed to track customer behavior, customer reactions to a new product. Data can be ordered or scattered, have different structure and density. If we talk about volumes, we are talking about gigabytes and terabytes of information. Processing such arrays manually is long and difficult, so automatic processing tools were created for analysis.

A Data Analyst (or Data Analyst) collects and analyzes big data, processes it, studies it, and draws conclusions. These experts conduct A/B tests, identify trends in customer behavior, and test hypotheses. The results of a data analyst’s work help businesses make objective decisions and reduce risks when launching new projects.

To do his or her job well, the data analyst must have a complete understanding of the company’s business processes. You need to know what task his work is supposed to help solve.

A data analyst can also look for patterns in a set of data, interpret the results, and make predictions to improve performance.

The post What does a data analyst do? appeared first on Da-Research PR.

Let’s look at five professions to see how they differ from each other.

• A systems analyst is a specialist who develops software requirements. He helps to solve company problems and set up business processes. A systems analyst solves the problem of functionality implementation from the technical side and makes a task pool for developers.
• A business analyst identifies the company’s problems and customer needs. Based on the findings, the business analyst decides which features should be integrated into the software in order to improve the final product.
• The market analyst collects information about the market, customer behavior, and target audience. He uses the data he collects to adjust the company’s marketing strategy.
• A data scientist collects information and then analyzes it. With the help of this knowledge he makes forecasts, identifies the probability of their implementation and obtains other valuable information that can be useful to the business.
• The Product Analyst is a specialist who collects and analyzes data on customer behavior and their interaction with the product. As a result of the product analyst’s work, the company receives clear recommendations that help the business grow and develop.
• It seems like these specialties are completely different. But if you look deeper, it turns out that they all require similar actions, knowledge, and skills. Once you have mastered one of the analytical professions, you can eventually change the field of work. For example, you can change from Data Analyst to Data Scientist.

The post How a Data Analyst differs from a Data Scientist and other analysts appeared first on Da-Research PR.

Virtuoso with all the spreadsheets, three-meter-long formulas, and conditional formatting. They neatly stack limitless files in different folders, naming each one with a phrase only they understand.

Strengths:

Careful and diligent. Send information to the customer only when they are really sure about it;
Have a good command of Excel tools, almost any task can be solved with its help.

Weaknesses:

Think there’s no point in learning new things if half the problems can be solved with two summaries and three vlookups;
prefer tables, lots of tables. Visualization, reserch, and analysis are more difficult, especially if research results need to be explained to non-analysts.

Recommendations: Excel analysts can be relied on for the most boring tasks. But it is time for them to switch to modern tools – databases and BI-systems.

Doubting analysts.

They start their conclusions with the words “approximately” or “most likely.” A common reason for this uncertainty is poor analytics infrastructure. A company has some ETL process crashing almost every day, or databases failing. So analysts can only say that conversions on branch A are 2-20% better than conversions on branch B.

Strengths:

Don’t mislead users and remind everyone that the data is inaccurate;
Honestly try to find the most accurate result and do everything to solve data problems;
Know everything about working with ETL processes, APIs, and database servers.

Weaknesses:

Get used to uncertainty about data and doubt results, even when working with complete data;
doubt the importance of what they are doing because they realize that incomplete analytics are hard to rely on.

Recommendations: push for infrastructure improvements with all your might. Talk about problems and push forward with goals to improve data collection.

Backlog Specialists

Their task lists are longer than the River Nile. They work from 8 a.m. to 8 p.m. Analysts prioritize, make priority lists, and then prioritize lists and still can’t get out of the perpetual backlog. Everyone around here knows that BI will answer any product question. That’s why you can find ETL process setup, building ML models, and collecting events across systems in the backlog.

Strengths:

Work without vacations or lunch breaks, trying to do as many tickets as possible. But the more tickets you do, the more they get thrown at them;
Are well versed in everything. They have the connections between all the tables saved in their heads. If you don’t understand why a country is written instead of the user’s email and the browser is in the country column, ask the analyst – he’ll tell you.

Weaknesses:

Because of the heavy workload, the backlog analyst doesn’t finish tasks – there just isn’t enough time to debug the processes 100%;
not enough time to offer ideas;
burnout. For the last year at the company, backlog analysts have been working out of their last legs. When they quit, they want to lie down and not see Jira for a couple of months.

Recommendation: this type of analyst should clarify the business necessity of all the questions that are asked. If you recognize yourself in this type, create a list of questions that the person must answer before giving you a task.

Analyst kings

Sprinkle ideas for product improvement, attend all the rallies and task tests, and if the decision to test or improve has passed them by, consider it a personal insult. Think they always have the last word and ask not to be assigned small tasks because they are built for more.

Strengths:

Are great at understanding the product, worrying about all metrics and imprudences;
Going into product managers where they can confirm their own hypotheses and see the numbers for themselves.

Weaknesses:

You have to look for a jun or midle to join such a specialist’s team for the “black job.” Because the analyst-king is a high-flying bird, and will not analyze 10 AB-tests a week;
Do not always hear other people’s opinions – there may be difficulties in communication.

Recommendations: listen and hear your colleagues. The product is made by the team. Even an excellent analyst needs help in development, design and other aspects.

Data Science analysts.

They have a huge technical background. Even if they need to divide 8,000 by 10, they turn on the Jupiter Notebook. Data Science analysts try to take on complex tasks on forecasting, segmentation, and models, but they keep getting asked to calculate budgets, find a couple of KPIs, or build a nice graph.

Strengths:

Versatile professionals who tackle tasks of any complexity. They have a wide range of hard skills and alternate between dashboards in BI systems and neural networks;
Solve most complex problems faster with programming languages.

Weaknesses:

Are willing to do all incoming tasks and often shy away from taking on more interesting projects worthy of their experience and expertise.
With Data Science analysts, you can solve complex problems. But you have to find common ground first.

The post 5 types of analysts. Determine who you work with appeared first on Da-Research PR.

Google Analytics is a fantastic example of descriptive analytics in action. It gives you a snapshot of what’s going on with your site. For example, how many visitors you’ve had over a period of time or where they came from. Similarly, systems like HubSpot will show you how many people opened a particular email or participated in a campaign.

But then there are two main methods that come into play in descriptive analytics; data aggregation and data mining. The process of collecting and presenting data in an aggregate format is known as data aggregation.

As a result, descriptive analytics compresses huge amounts of data into a clear basic summary of what happened. As we will see later, this is often the starting point for deeper analysis.

№ 2. Diagnostic analytics (Why did this happen?).
This is the type of analytics that tries to find out why something happened by digging deeper. The main goal of diagnostic analytics is to find anomalies in your data and respond to them. For example, if your descriptive analysis shows a 20% drop in sales in March, you’ll want to find out why. Diagnostic analytics basically helps you do that.

Diagnostic analytics applications.
Using this type of analytics, the analyst looks for any new data sources that might provide more information about the causes of the sales decline. They may go further and discover that despite a large number of website visitors and a large number of “add to cart” actions, only a small percentage of visitors actually make a purchase. Further investigation may reveal that the majority of customers dropped out during the time they entered their shipping address.

№3. Predictive analytics (What happens in the future?)
Predictive analytics seeks to anticipate what will happen in the future. However, this is possible based on past patterns and trends to help estimate the likelihood of a future business event or outcome.

Predictive models literally create predictions based on relationships between a set of variables. For example, you can use the correlation between seasonality and sales numbers to predict when sales will drop. So, if your predictive model predicts that sales will drop in the summer, you can use that information to create a summer-themed advertising campaign or cut spending elsewhere to make up for the seasonal drop.

On the other hand, you may be running a restaurant and want to know how many takeout orders you’ll get on a typical Saturday night. The results of this type of analytics can help you decide whether to hire an additional delivery driver.

№ 4. Prescriptive analytics (What’s the best course of action?).
To help determine the best course of action, prescriptive analytics examines what happened in the past, why it happened, and what might happen in the future. In other words, prescriptive analytics explains how best to take advantage of descriptive, diagnostic, and predictive analytics.

Nevertheless, it is the most complex type of analysis. This is because it involves many things, including machine learning algorithms, statistical approaches, and computational modeling procedures.

Essentially, a prescriptive model evaluates all the different choice models or paths a corporation might take, as well as their likely consequences. This allows us to visualize how each set of decisions might affect the future, as well as quantify the impact of a particular decision. Moving forward, the organization will be able to determine the best routes based on every conceivable scenario and consequence.

Predictive models are similarly used to calculate all the different “routes” a corporation can take to achieve its goals; with the best option in sight. And knowing which actions to take for the best chance of success is a tremendous asset to any company. So it’s not surprising that prescriptive analytics plays such a big role in business.

The post What types of analytics are available appeared first on Da-Research PR.

When it comes to business information, CIOs (CIOs) and data directors (CDOs) are tasked with bringing order to the chaos.

As companies collect more and more data, they face both commercial pressures to do more with the information they have, and growing regulatory requirements for data management, especially if it involves customers.

The situation is further complicated by the range of tools available to store and manipulate data, from data lakes and data centers to object storage, machine learning and artificial intelligence.

According to a study by Seagate, up to 68 percent of business data goes unused. As a result, companies are missing out on the benefits that data should provide. At the same time, they face regulatory and compliance risks if they don’t know what data they have and where they store it.

To solve this problem and make data work for the business, companies need to look at their data architecture. At its simplest level, data architecture is about knowing where an organization’s data resides and mapping how data flows through it. However, given the myriad of data sources and ways to manipulate and use data, there is no single blueprint for this. Each organization needs to create a data architecture that meets its own needs.

But part of the problem for CIOs and CDOs is that technology leads to increased complexity in both data management and use. As the consulting firm McKinsey noted in 2020, technical additions — from data lakes to client analytics and streaming platforms — have made data architecture extremely complex. This makes it difficult for companies to manage existing data and deliver new capabilities.

The shift from traditional relational database systems to much more flexible data structures – and the collection and processing of unstructured data – gives organizations the ability to do much more with data than ever before.

The challenge for CIOs and CDOs is to connect these capabilities to business needs. Creating a data architecture should be more than just maintaining IT or ensuring compliance.

What is data architecture
A data architecture is often described as a data management scheme. Of course, an effective data architecture must map the flow of information within an organization.

This, in turn, relies on a good understanding of the data being collected and stored, the systems in which it is stored, and the regulatory, compliance and security regimes that apply to that data.

Companies also need to understand which data is critical to operations and which data provides the most value. As organizations store and process more and more information, it becomes increasingly important. Sometimes it’s more art than science.

Data architecture must be tied to an organization’s data strategy and data lifecycle, but it also depends on sound data management.

Often organizations divide their data architecture into two parts: data provisioning and data consumption or use.

On the provisioning side, CIOs and CDOs need to look at data sources, including transactions, business applications, customer actions and even sensors. On the data consumption side, it can be about reporting, business intelligence, advanced analytics, and even IO and AI. Some companies may also seek to further use data by selling it or using it to create new products.

Why and how to implement data architecture
The reason for creating or updating a data architecture can be either a change in technology or a change in business.

Changing the core component of an organization’s IT or analytics systems provides another way to look at data flows. And moving to the cloud offers a way to update data flows without having to lift and shift systems. At the same time, changes can be made for each application or each project.

The shift from data warehouses to data lakes also facilitates this, as data no longer has to be tied to specific applications.

The post How to develop a useful data architecture for business appeared first on Da-Research PR.

Which argument is closer to the judge, in whose favor does he decide more often, and how fast does he do it? If it takes a person weeks to search, collect and then analyze court data, the software will spend hours doing it.

Large analytics platforms for attorneys who represent clients in court can “scan” all the cases that a particular judge has tried, collecting and storing information from a wide range of sources: for example, from the judge’s records or decisions. Such a platform then analyzes the collected data and builds a model of the judge’s reactions to the attorneys’ actions based on the analysis. It can determine which motions are most likely to be accepted or denied by judges, how often dissenting opinions will be used, and what precedents they are most likely to use in their decisions. Such platforms will also help law firms better understand how to conduct themselves in court: for example, they can bring in an expert who has appeared in a similar case or a lawyer who has extensive experience working with a particular judge.

In the U.S., where court information is public and not too difficult to find, there are at least a dozen analytical companies that provide such services. Among them, for example, are Litigation Analytics, Ravel Law, LexMachina and Premonition. “These days, everyone has baseball analytics (a reference to the movie Moneyball – The Sphere). Soon it will be the same with entitlement. And when everyone starts using it, it will all come down to how well you know how to use data,” the Financial Times quoted Josh Becker, head of Lex Machina, as saying.

Investors investing millions in predictive forensic analytics also believe this. Back in 2018, for example, California startup Gavelytics, which specializes in judicial analytics, received $3.2 million in funding to develop the project. In 2019, a similar project launched in the same state with an expanded geography – demand for such services is gradually growing.

Five years for the future
Data analytics can be used in both criminal and civil cases. For example, popular areas in the U.S. are intellectual property rights protection and patent cases. However, as FT author Barney Thompson notes in his article “Big Data: How Law Firms Play Moneyball,” there is one big problem for American lawyers: an almost complete lack of information on civil cases. According to him, almost 90% have either ended in settlements or are simply dismissed because it is extremely costly to run a long process.

The post Reading minds: how data analytics predicts judges’ decisions appeared first on Da-Research PR.

• Medicine – making a diagnosis based on disease symptoms, identifying factors that provoke disease, determining the propensity to become ill in the future, forming recommendations and prescribing drugs to treat and prevent illnesses.
• Advertising and marketing – determining the effectiveness of promotional campaigns, identifying the most effective channels and forms of presenting information (personalized targeting), building referral systems, creating demand based on user interests and his behavior in the network, predicting and preventing customer churn (Churn Rate), and optimizing pricing.
• Insurance and crediting – determining the exact amount of compensation or credit, scoring the client. For example, it is already implemented in a joint project between banks and Yandex, when banks evaluate the solvency of a potential borrower based on the history of his requests in the search engine.
• Industry – identifying key factors that affect product quality and the performance of production processes, predicting equipment failures, scheduling preventive inspections and equipment repairs, forecasting product demand, optimizing production capacity utilization and warning of future emergencies.
• Finance and security – detection and prevention of fraudulent operations (anti-fraud systems), detection of malicious programs and data leakage cases.
• Human resource management (HR) – identifying key factors that influence employee competencies, creating a professional competency model, forecasting layoffs, preventing professional burnout and workplace conflicts.

Implementation of analytical Big Data systems is a complex step-by-step project that is often performed as part of business digitalization. Predictive analytics is at the top of the pyramid and relies on the previous levels: predictive, diagnostic, and descriptive. Therefore, in order to form optimal management decisions based on data, it is necessary, first of all, to accumulate a relevant amount of this information, sufficient to correctly train Machine Learning algorithms. Some analytical tasks are solved with the help of modern BI-tools, such as commercial platforms like Oracle Data Mining, SAP BusinessObjects Predictive Analysis, SAP Predictive Maintenance and Service, IBM Predictive Insights or open-source solutions (KNIME, Orange, RapidMiner). In practice, many enterprises that have embarked on the path of digital transformation, create their own systems of big data analytics. They use a variety of Big Data technologies, for example, Apache Hadoop – for storing information (in HDFS or HBase), Kafka – for collecting data from various sources, and Spark or Storm – for fast analytical processing of streaming information. In particular, this is how the recommendation system of the streaming service Spotify is implemented, which we described here. Thus, the organization of predictive and, even more so, prescriptive data analytics is one of the key challenges of digitalization of business.

The post Where Big Data analytics systems are used and how to implement them appeared first on Da-Research PR.