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How IBM does data science consulting? In 2020 IBM does data science consulting by following their vetted best-practice framework.We will focus on a fascinating topic – the step-by-step process IBM’s data science team applies when working on a consulting project. We believe this overview can be highly beneficial for both experienced professionals and data science beginners. We will explore a best-practice framework applied by one of the pioneer and leading companies in the field. This way, you’ll get an insider’s look at how a consulting project that involves data analysis and data science unfolds. In addition, we will examine the results achieved in IBM’s data science consulting projects with major clients from different industries. Why is that important? Well, each of these initiatives serves as an invaluable lesson to the rest of the companies in the respective industry. If, for example, Carrefour managed to leverage AI to improve its supply chain processes, the rest of the global hypermarket chains would basically be obliged to follow, if they want to keep up. Let’s get right in and outline the five stages of a data science consulting project.
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The coefficient of variation, variance, and standard deviation are the most widely used measures of variability. We’ll discuss each of these in turn, finishing off with the coefficient of variation.Variance measures the dispersion of a set of data points around their mean value. Population variance, denoted by sigma squared, is equal to the sum of squared differences between the observed values and the population mean, divided by the total number of observations. Sample variance, on the other hand, is denoted by s squared and is equal to the sum of squared differences between observed sample values and the sample mean, divided by the number of sample observations minus 1. While variance is a common measure of data dispersion, in most cases the figure you will obtain is pretty large and hard to compare as the unit of measurement is squared. The easy fix is to calculate its square root and obtain a statistic known as standard deviation. In most analyses you perform, standard deviation will be much more meaningful than variance. Alright. The other measure we still have to introduce is the coefficient of variation. It is equal to the standard deviation, divided by the mean. Another name for the term is relative standard deviation. This is an easy way to remember its formula – it is simply the standard deviation relative to the mean. As you probably guessed, there is a population and sample formula once again. The post Variance, Standard Deviation, Coefficient of Variation in 2020 appeared first on Data Science PR.
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‘Data Scientist’ is one of the fastest-growing jobs in recent years. It’s an exciting and highly paid career, that presents you with tons of opportunities for development. So, what are the skills you need to become a data scientist in 2020?We’ve been doing this research for 3 years now, and in this video, we’ll share the top skills that will make you successful in this super-competitive field. In 2020, our study portrays a data scientist’s collective image as a male (71%), who is bilingual, has been in the workforce for 8.5 years (3.5 years of which has worked as a data scientist). He or she works with Python and/or R and has a Master’s degree. You can’t become a data scientist without a strong programming skillset. And in 2020, general-purpose languages are used more extensively by data scientists than ever before. According to our own annual research, 74% of current data scientists are proficient in Python, 56% use R, and 51% – SQL. If you know that you want to become a data scientist, it will be beneficial to study the career path of others who have taken the data scientist career path and learn from their experience. We hope that this video was useful and will guide you in the right direction if you decide to pursue a data scientist career path! The post Skills Needed to Become a Data Scientist in 2020 appeared first on Data Science PR.
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How to Become an SQL Developer in 2020? We’d love to explain, so let’s discuss how to become an SQL developer in 2020.We’ll describe an SQL developer’s role in a company. Then, we’ll focus on the technical and soft skills you need to be successful on the job. We’ll also discuss the education and working experience hiring companies are looking for. To top things off, we’ll provide information regarding an the expected salary for SQL developers in different parts of the world. So, what does an SQL developer actually do?In short, we can say that this position requires you to build, maintain, and manipulate database systems. And, very often, you’ll have to use the data stored in the databases you created to develop ad-hoc and recurring reports. To this end, you will need to write and test SQL code, as well as create stored procedures, functions, and views. Let’s discuss some of the technical skills an SQL developer needs on the job. Naturally, you need to be proficient in SQL. I’m sure you didn’t see this one coming. Some of the most popular database management systems that allow you to work with versions of the Structured Query Language are MySQL, SQL Server, and PostgreSQL. What formal qualifications do you need to apply as an SQL developer?This is a position that is a suitable position for junior professionals. However, in most cases, you need some initial experience. Almost all job ads we analysed required 1 or 2 (and sometimes more) years of experience with SQL and relational database tools in a professional environment. The post How to Become an SQL Developer in 2020? appeared first on Data Science PR.
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The market for Data Science has been growing extensively over recent years. As a result, the position of data scientist has emerged as a truly attractive career path option with an abundance of rewarding job opportunities. So, to help you stay at the forefront, we have conducted an in-depth study on job offers in the field of data science. We have extracted valuable information of the company offering the position, the required educational credentials, and sought-after work experience, as well as desired skills and techniques involved. That was our compelling look at a sample of 1,170 job offers for the position of data scientist. Hopefully, you have found some of this information useful and advantageous for you in your path to landing your dream data scientist job. The post Data Scientist Job Descriptions in 2020 appeared first on Data Science PR.
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Data Science or Computer Science degree is best for data science? Here we’ll discuss whether a Data Science or Computer Science degree is the best choice for you. Well, both D-S and C-S are fantastic choices for a concentration, so please don’t feel discouraged if you’ve already chosen one over the other. That being said, we here at 365 have conducted research to determine which one is better for a successful career as a data scientist. We’ll begin by weighing the pros and cons of earning either degree, starting with D-S. Then, we’ll do some evaluation and head-to-head comparison before picking a winner.
Of course, the main reason is that potential employers believe you have a great interest in the job. They don’t have to worry about programming skills, analytical understanding of statistical results, or your data-storytelling abilities. This is crucial because some great statisticians lack the coding pedigree, while some programming wonderkids lack the knowledge to extract insights from a dataset. With a Data Science degree, you’re sure to possess all the necessary qualities, without needing outside validation, like extra certification. However, currently, there is 1 major con when it comes to a Data Science degree – availability. Since the field is relatively new, a Data Science program can sometimes be hard to come by, regardless of whether we’re talking undergraduate or graduate programs. The scarcity has resulted in many students having to pick alternative concentrations and, as a result, losing interest in the field prior to graduating… The post Data Science vs Computer Science Degree for Data Science Career in 2020 appeared first on Data Science PR.
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Before you start sending out your resume to Bain and McKinsey, consider our list of the Best Data Science Startups to Work For in 2020!Why work for a data science startup? Sure, big data science consultancies have the stability and the benefits every aspiring data scientist strives for. However, you may find yourself working on predictable and often repetitive tasks with little opportunities for growth. At least for the first few years. Startups, on the other hand, allow you to develop your skillset by trying new things and handling a variety of challenges. Responsibilities there change quite frequently. So, within less than a year you could be doing something entirely different… And a lot more interesting for you than what you were initially hired for. In other words, the sky is the limit! So… watch our data science startups review to learn what they do, where to apply, and why you should consider working there. And don’t mind the order – these startups are so unique, that every single one of them could easily be number 1 on our list. The post List of The Top 10 Best Data Science Startups to Work for in 2020 appeared first on Data Science PR.
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Since we are talking data science, the only logical way to approach the question is to ask the data. And that’s what we’ve done for 3 consecutive years. Since 2018 we have explored 1001 data scientist LinkedIn profiles to uncover the most interesting trends in the data science field. In this video we will go through the most important findings from the last 3 years. In fact, we have created a very cool and interactive PowerBI dashboard which you can use to analyze the data yourself here. According to the data, the average data scientist from 2018 to 2020 is a male with a second-tier degree, coming from a quantitative background, which is not necessarily data science or computer science. Their preferred programming language is Python, but they’d often know R and SQL. Many of the new data scientist positions are being filled by people who are already data scientists, so the field feels much more saturated. Getting into data science still looks like a great opportunity, but the ‘data scientist’ position becomes more and more exclusive. Our sample shows that at least 80% of the people held a minimum of a Master’s degree. This isn’t as surprising, considering data science is a field that expects advanced know-how from the person — usually achieved by graduate or postgraduate types of education, or independent advanced research in other cases. The post What Do You Need to Become a Data Scientist in 2020 vs 2019 vs 2018? appeared first on Data Science PR.
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The evolution of technology has totally changed the world we live in, but is it moving too fast? We built a timeline to find out! The post Evolution of Technology And the Inventions that Changed the World appeared first on Data Science PR.
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Who’s the data engineer and what do they do?Data engineers are the Jedi Knights of data science. They rely on a blend of analysis, wisdom, experience, and judgment to make key decisions for a company’s success. A data engineer is a self-starter who is inspired to complete more than your usual number of tasks. Data engineers are the ones to take things further up the data science pipeline. They use the data architects’ work as a steppingstone and then pre-processes the available data. These are the people who ensure the data is clean and organized and ready for the analysts to take over. Data engineers also implement complex, large scale big data projects with a focus on collecting, managing, analyzing and visualizing large data sets. They are also the ones who turn raw data into insights using various tool sets, techniques, and cloud-based platforms. That said, for a data engineer, knowledge of data modeling for both data warehousing and Big Data is a must, along with experience in the Big Data space (Hadoop Stack like M/R, HDFS, Pig, Hive, etc.). Of course, the ability to write, analyze, and debug SQL queries will helps the aspiring data engineer score high on any employer’s recruitment list. In terms of soft skills, they are great team-players. A data engineer knows how to actively collaborate with data scientists and executives to build solutions and platforms that meet, or even exceed a company’s business needs… The post How to Become a Data Engineer in 2020? appeared first on Data Science PR. |
Data ScienceData Science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data Science is related to data mining, deep learning and big data. Archives |