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Data Analyst Skills and Qualities That Will Turn You into A Professional

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Most businesses now understand the advantages of a data-driven business strategy and decision-making. Thus, these businesses know the importance of hiring skilled data analysts to do designated tasks which will help them offer insight into the ongoing stream of information being gathered.

Data analysts must apply a wide range of talents, from in-depth analysis to data visualisation and storytelling. You could be writing a SQL query to study a data set one moment. Then you could be explaining to a board of directors how the company needs to change in light of your findings.

What Does a Data Analyst Do?

A data analyst is a person who uses technical knowledge to analyse data and offer insights. A data analyst analyses data to discover important customer insights and useful applications for the data. They also share this information with the company’s management and other stakeholders.

Data analyst job roles also include data collection from primary and secondary sources. Performing statistical analysis of the data and database implementation and maintenance are part. Also trend identification, analysis, and interpretation.

Data Analyst Skills and Characteristics That Will Make You Professional:

Data Cleaning

A data analyst’s role includes retrieving data from one or more sources and then processing it to remove irrelevant data. This preparation is done on this for a better category and numerical analysis. Resolving missing and inconsistent data is another aspect of data cleansing.

For instance, when you get data from reviews, most of the time, the reviews include unnecessary and irrelevant comments. Removing them before conducting data analysis enables you to make decisions using the highest-quality information.

Statistics Knowledge

Statistics and probability are crucial data analyst abilities. Understanding statistics also help you confirm the validity of your analysis. It also helps prevent frequent logical fallacies and mistakes. Depending on the data you’re dealing with, a different level of statistical knowledge may be required.

R or Python Programming

R and Python are utilised to conduct sophisticated analyses on large datasets. They are also used for predictive analytics. They both adhere to industry standards. At least one of these languages must be mastered by you.

Employers don’t care which programming language their employees use as long as their analyses are accurate. This is because both R and Python are open-source and free. However, some analysts prefer R over Python for exploring data sets and performing ad-hoc analysis. This is because it was created specifically for analytics.

Data Analysis and Exploration

Data analysis, in its most basic form, entails taking a business requirement and evaluating pertinent data. This will help create a response to that question. Data analysis is also the methodical application of statistical approaches to analyse data.

Exploration is a different type of data analysis. Finding fascinating patterns of data that have the potential to benefit a business is called data exploration.

Critical Thinking

Data analysis is more than just looking at it; you must also understand it and take into account its implications beyond the scope of the numbers.

Critical thinking enables you to evaluate information, identify trends, and derive useful knowledge and concepts. You need to work harder and make a commitment to thinking as opposed to just processing.

The most challenging aspect of using data to get answers is frequently identifying the right questions to ask in the first place. You must think analytically if you want to be successful as an analyst. A data analyst’s job is to find and combine correlations that aren’t always obvious.

Communication

In the end, you must be able to explain your findings to others. No matter how intelligent and skilled a data analyst you are, if you can’t communicate the patterns you find to people who don’t understand technical terms, you’ve failed.

Being a successful data analyst requires being “bilingual.” You should be able to discuss highly technical topics with your trained colleagues. Then present concise, high-level explanations in a way that aids decision-makers.

Create Data Visualisation and Presentation

Data visualisations help to clarify trends and patterns in the data. Because most people understand charts and graphs more quickly than they do spreadsheets. This is because humans are visual beings.

Data analysts must explain their conclusions clearly by using high-quality charts and graphs. One option is to utilise a complex dashboard with interactive data elements. Also, straightforward chart and table with date filters.

Presentation abilities and data visualisation go hand in hand. But it’s okay if presenting doesn’t always come easy to you! Even seasoned presenters occasionally experience performance anxiety. Start by practicing, then practice some more until you find your rhythm, just as with anything else.

Machine Learning Skills

A grasp of machine learning has been regarded as a crucial element of an analyst’s toolset. This is because AI and predictive analytics are two of the hottest subjects in the field of data science. In the job market, having a basic understanding of related tools and concepts could give you an edge over competitors.

Attention to Detail

Data analysis requires a keen eye for detail. You should be able to locate and recognise the tiniest hints of a larger message that is concealed in plain sight in a massive data collection.

Building systems for effectively acquiring and sorting data can also benefit from having a strong eye for detail. A single coding mistake might jeopardise your entire operation. As a result, whenever handling data, you should always be on the lookout for any minor errors that could turn into more serious issues.

Research Skills

You might believe that data analysts have immediate access to all the knowledge they require to carry out their duties. Contrary to popular belief, data analysts still need to research to get all the information they want and to maximise the use of the data at their disposal.

To properly contextualise your discoveries when making visualisations or interpreting raw data, you must feel at ease conducting more research. Additionally, after you give it to your audience, it should be clear to them.

SQL and Excel

The most crucial competency for data analysts to possess is probably the ability to use SQL or Structured Query Language. It is the de facto industry standard database language. It is sometimes referred to as an advanced Excel counterpart. This is because it can work with much larger datasets than Excel can.

Almost all businesses require SQL experts to manage and store data and connect various databases. For example, the ones Amazon uses to suggest products you might like) or create entirely new database structures. Advanced Excel techniques VBA lookups and Macros are utilised for queries and quicker analytics.

Conclusion

As technology progresses, data analysis is becoming more and more prevalent. As more businesses, enterprises, and organisations switch to automated solutions, the need for data analysts will increase. Data collecting should not be the extent of your responsibilities as a data analyst.

If you also possess the aforementioned abilities, you can have a successful career in data analytics. You will become the ideal candidate for a data analyst position by developing these skills. This will raise your likelihood of landing a job.

Amazon Workers Will Soon Be Replaced by Robots

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A decade from now, according to Jeff Bezos, the founder of Amazon, robotic systems will be sophisticated enough to grab objects with human-like dexterity. Amazon appears to be moving closer to that objective three years later.

In the retail and e-commerce sectors, warehouse automation is more important than ever, particularly for Amazon, which is the largest online retailer and the second-largest employer in the United States. Recode revealed in June that internal study at Amazon suggested that if it did not make a number of significant changes, such as boosting automation in its warehouses, it would run out of people to hire in the United States by 2024.

The corporation is simultaneously dealing with the possibility of US workers beginning to organise after the Amazon Labor Union won the historic Staten Island vote, as well as another union election that will take place in Upstate New York in October. For years, labour groups have conjectured that Amazon may increase its automation efforts in reaction to unionisation efforts.

It’s not as futuristic as you might think to look at the robotic arm in question. In place of a new grabbing mechanism, the proof-of-concept machine uses an ordinary metal pincher. But every three seconds, it may pick up a fresh object and drop it onto a metal chute.

According to Amazon, the robot could pick and store products at a rate several times faster than a human worker could at the rate it is moving in the video—more than 1000 items per hour. Each object is seized and handled without any human direction, from a crayon box to a bottle that appears to contain garlic powder to a whisk broom.

Multiple cameras are used by the robot to help it “see” the variety of items in front of it. Machine learning is also used to help the robot decide how to pick up a particular item, and motion-planning algorithms are used to help the robot move through the crowded area without bumping into or damaging any of the goods. Additionally, according to early tests, the robot breaks some objects considerably less frequently than other manipulation robots that Amazon has examined.

It’s unclear how long it will take Amazon to develop a single robot that can handle the majority of products, but “when,” not “if,” will this happen. One of the biggest mysteries of this age of automation will be resolved when the “when” becomes “now.” Will the task be easier or better for those doing these occupations if a new generation of warehouse robots can grab objects almost as well as human hands? Or will the development of technology make these workers and their employment unnecessary?

8 Effective B2B Marketing Strategies to Help You Reach Your Goals

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The B2B industry has been growing at a fast pace in recent times. As a business owner who wants to succeed in selling to other businesses, you’ll need to develop the best strategies. If you want to build a successful B2B business, make sure you research the best strategies.

This article will explore some of the marketing strategies you can use as a B2B business owner. By the end of it, you should be able to take your business to the next level. Read to the end to find out how you can get started with marketing your B2B business.

Let’s get started with it.

1. Account-Based Marketing

One strategy that will prove to be effective for B2B marketing is account-based marketing. It entails targeting customers with their account details and needs in mind. This technique involves using consumer behaviour research to understand your target customers.

You will build and strengthen customer relationships better if you use this strategy. Clients feel more appreciated when a business tailors communication specifically for them. This is why account-based marketing increases the chances of convincing and retaining customers.

2. Omnichannel Marketing

You can also consider adopting omnichannel marketing for your business. Many people thought that this was only a digital tactic that businesses use to improve their technology. However, the reality is that this technique works perfectly for marketers in the B2B industry.

You should research the channels that your audience uses to communicate. Then ensure that you communicate with them on those channels. Being reachable on all the channels your audience uses will boost the customer experience and help improve your marketing.

3. Hyper-Personalisation

This is another way of personalising the messages you send to your audience. However, it is all about personalising experiences with proper timing. For instance, it entails sending your marketing messages at the right time. This is done by using data analytics, automation, and AI.

There’s a lot of marketing research B2B strategies that you must do to ensure that you personalise marketing messages well. The good news is that there are various tools that will help you get the best results. This article has discussed some of them, including AI and analytics.

4. Artificial Intelligence and Machine Learning

You cannot go wrong with marketing through AI and machine learning. You can use these two technologies to automate your campaigns and attract more customers. This also includes using interactive chatbots for apps and websites to send messages to customers.

Chatbots are increasingly becoming popular in the B2B world. They are helping businesses become more interactive and provide customers with the best services. Also, they are making it easier to personalise messages for your clients, which is good for marketing.

5. Email Marketing

Marketing through emails can be a great idea for your business. Almost every person in the world today has access to the internet. And many people use email for both personal and business communication. This makes it an excellent idea to consider using email marketing.

As a B2B owner, email marketing will be a great way of generating revenue. It will make it easier for you to power your game and discover new content. Many market research companies leverage it because of how effective it is for marketing and feedback gathering.

6. Marketing on Social Media

So much has changed since social media started gaining traction in the business world. One of the most notable changes is how easy and efficient marketing through social media has become. This is going by how people carry their phones and check social media for updates.

You need to take advantage of the addictive nature of social media. In the B2B field, there are business managers and owners who are social media users. They can come across your posts and suggest that their business procures products or services from you.

Social platforms such as LinkedIn have proved to be effective for marketing professionals. It has also become easier to do cross-platform marketing. For instance, you can share posts from Facebook to Instagram. You can link social media pages to your business website.

7. Influencer Marketing

It might seem new, but it has quickly gained ground in the business world. It was a popular technique for B2C interactions, but that’s changing quite fast. Today, you can also market to other businesses using influencers, especially to create posts on social media.

A business that leverages influencer marketing focuses on benefiting from the following the influencer has on social media. This can help increase a business’ sales velocity by boosting conversion and retention. Also, profits and the business’s market share will improve.

8. Search Engine Optimisation

SEO is another marketing strategy that will work for a business in any industry. You cannot go wrong with it if you want to build a successful business. SEO will help ensure that your business grows by improving your page rank and visibility.

The reality with search engines is that rankings matter a lot. Most people don’t click beyond the second page of a search result. Thus, it is vital to ensure that your website ranks on the first page. Investing in SEO will make it easier to ensure your B2B business generates sales.

You can hire an expert to help you grow your search engine rankings. This will require you to invest as much as you can in research. Working with the best paid market research companies will make this easier. It will help you identify where to improve for better SEO.

Conclusion

There’s a lot of research to do if you want to succeed as a B2B owner. Thankfully, this article will save you hours of research to ensure you get started with marketing for B2B. This will ensure your business starts to reap the desired benefits from marketing within a short time.

You now know the steps to follow to ensure you win as many customers as possible. This post is the guide that will help increase your chances of succeeding as a B2B business owner. The primary goals should be attracting and retaining customers and making profits.

Best Online Courses for Learning Machine Learning

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Machine learning is a branch of Artificial Intelligence (AI) that deals with the design and development of algorithms that can learn from data.

The demand for Machine Learning skills is growing rapidly. The global market for machine learning is expected to grow from $12 billion in 2017 to $47 billion by 2022, according to research by MarketsandMarkets.

Machine Learning is an extremely powerful tool that has been widely used in many industries. It has been applied to areas like healthcare, finance, law enforcement, and even education.

Here are some of the best courses to learn machine learning online.

Best Online Courses to Learn Machine Learning

The 10 best online courses for learning machine learning are:

  • Udacity’s Intro to Machine Learning course
  • Google’s Machine Learning Crash Course
  • Andrew Ng’s Machine Learning course on Coursera
  • Stanford University’s Machine Learning course on Coursera
  • Johns Hopkins University’s Introduction to Data Science Course on Coursera
  • MIT’s Introduction to Computer Science and Programming with Python Course on edX
  • UC Berkeley’s Big Data Analysis with Scala and Spark Course on edX
  • UC San Diego Extension’s Python for Data Science Course on edX

EdX vs. Coursera vs. Udacity: Which One is Best?

The three major players in the MOOC space are Coursera, edX, and Udacity. All three of these companies offer different courses and degrees in a variety of subjects. There is no clear winner among these three companies.

Coursera is a for-profit company that offers many of their courses for free but charges a fee to get a certificate at the end of the course. They also offer paid degrees in subjects like business administration, computer science, and social sciences. Coursera has over 20 million users around the world.

edX is a non-profit organisation that partners with universities like MIT and Harvard to offer online courses from those schools for free to anyone who wants to take them. edX has over 11 million users around the world and offers online degree programs in subjects like computer science, business management, and biochemistry.

Udacity is a non-profit organisation that offers online degrees in computer science and other technical fields. Udacity has over 2 million users around the world.

Often times people ask, which is better for career Udacity or Coursera? Udacity offers a more detailed curriculum and career opportunities that would be unavailable to most people at Coursera. Udacity is also able to offer a more customised online experience with face-to-face mentoring, which helps students better understand the material they are learning.

Conclusion

Machine Learning is an area of Artificial Intelligence that is growing in popularity. It’s a branch of AI that deals with algorithms that can learn from data and make predictions. or decisions on their own.

Machine learning algorithms rely on training data to figure out patterns and make predictions. Training means that the machine will learn by looking at examples and deciding what is relevant, what is not, etc.

There are two types of machine learning algorithms: supervised and unsupervised. For an example of a supervised machine learning algorithm, we can look at spam filtering. You can train a spam filter to detect spam emails by showing it different emails and giving it instructions such as, what are the key words that this email contains?

Unsupervised algorithms do not need training data. These algorithms find patterns in the data themselves in some way and then make predictions from those patterns. For an example of unsupervised machine learning algorithm, we can look at image recognition. It is able to pick out specific images based on its own set of rules.

The best place to start learning Machine Learning is in the comfort of your own home with a free online course. It’s an interactive way to learn the basics and it doesn’t have any cost.

Technology-Led Innovations in the Supply Chain Industry

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For supply chains to run smoothly, each logistics element needs to be efficient and work seamlessly with the other links in the chain.

Inventory needs to always be at the right level, goods need to leave warehouses at the right time, and shipments need to be delivered within set timeframes, to name just a few of the things that are important to successful logistics operations.

Smooth-running supply chains are more achievable than ever thanks to technology-led innovations. So, let’s look at some of the innovative techs that are helping the supply chain industry to function even better.

Logistics Optimisation Software

When companies utilise logistics optimisation software, they can streamline their operations and ensure all components of the supply chain are working in coordination with each other.

Software solutions not only provide numerous tools for achieving multiple tasks, such as having access to real-time data to check order fulfilment statuses.

Logistics optimisation software also blends multiple technologies into a single cohesive platform, enabling logistical operations to run much more smoothly.

But supply chain software solutions do much more than just that.

Many logistics companies are beginning to understand how much value their supply chain data has beyond everyday operations.

For instance, with the best logistics optimisation software, companies can analyse trends and patterns, and even use predictive analysis to find innovative ways of expanding into new markets and making other supply chain improvements, before any risks arise.

The innovative technology of predictive analytics basically takes known information and uses it to create more efficient supply chain planning.

Data can be gathered from multiple company departments, such as marketing and sales, and used with logistics data to come up with analysis reports that can help supply chain managers create more efficient and profitable logistics solutions.

At the end of the day, with the right software, supply chain companies can measure logistics elements more accurately than ever before to make improvements that benefit the companies, their partners, and the end customers.

AI and Machine Learning

Today, artificial intelligence and machine learning play a key role in supply chain improvement, and the technologies will only become more sophisticated and game-changing in the future.

AI and machine learning tech can create fully-automated or semi-automated processes for supply chain optimisation. Because the technologies basically emulate human performance and knowledge and strive to go beyond it.

AI and machine learning can improve optimisation elements such as planning, forecasting, implementation, and maintenance in logistics operations.

By applying predictive modelling to third-party logistics and tightening data security, AI and machine learning can boost the end-to-end supply chain.

The tech can also provide full supply chain visibility to enhance the management of key performance indicators.

Inventory management, delivery routing, shipping transactions, and even customer service, can all become automated, and thereby improved.

Robotics

More and more specialist items are being manufactured by big robotics companies that can help in the day-to-day operations of logistics.

Automated robotic systems are now more capable and flexible than ever before.

For instance, robotics has led to warehouse operations becoming quicker. A robotic fast-packing system can handle thousands of picks per hour, thanks to the innovative technology that enables it to handle objects that are in all kinds of positions and orientations.

At present, robotic fast-packing systems are commonly used in supply chains for the mail-order and pharmaceuticals industries, because the systems do have limitations regarding things like size and weight, but research is gathering pace to create robots that can work in multiple settings and achieve human levels of adaptability.

Other robotics technology advancements include sensors and safety technologies that enable automated vehicles and robot arms to work alongside human workers.