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. via Data Visualization – Data Science PR https://ift.tt/3dgnHh8
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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. via Data Visualization – Data Science PR https://ift.tt/37G505x What are the best data science degrees to get hired in 2020? We did a lot of research and we can now share with you the best data science degrees in 2020!To get into Data Science, you need a degree that signals potential employers you are the qualified candidate they’re looking for. We have conducted several studies on this topic to determine what are the best degrees for an aspiring Data Scientist. So, in this article, we’ll go over the level, discipline and university rank you should be looking at when deciding what degree is worth pursuing or if your current degree is suitable for the field. Our results show that virtually all data scientists have graduated from an institution of higher education. This includes Bachelors, Masters, MBAs, and Ph.Ds. However, some degrees seem to be much more popular than others. In fact, only around 2% of all data scientists in our sample owned an MBA, but that’s not entirely surprising. If you decide to do an MBA, chances are you’re not aiming at the hands-on technical data scientist role on the team. Bachelors, Masters, and Ph.Ds round up roughly 95% of the data, with 75% being split among Masters and PhDs. This means that roughly 3 out of every 4 data scientists have at least a master’s degree. So, yes, going for a graduate program is highly recommended. Of course, if you think a B.A. is as high as you want to go, there is no need to be discouraged. Nearly 20% of the data scientists in our sample had only completed an undergraduate prior to entering the field. And while this number is not high, the percentage of data scientists holding only a Bachelor’s degree has been steadily growing over the last three years. This is a refreshing indicator that shows employers are starting to value skills over years of schooling. In other words, a qualified candidate today has a higher chance of breaking into the field, compared to two years ago. The post What Are The Best Data Science Degrees to Get Hired in 2020? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/30TlO7J How to Become a Data Scientist in India in 2020? Let’s find out! In this article we’ll look at the latest research to help you assess your chances of landing a data scientist job in India.We’ll focus on the education and qualifications you need to become a data scientist eligible for the job at any company. To learn all that and some extra tips that will help you get a data science job in India, watch this video! A recent report by The Hindu states that there are an estimated 97,000 data analytics job openings in India. Bengaluru accounts for 24% of these job openings, while Delhi/NCR for 22%. Since the data science field is progressing at an extremely fast rate, the huge demand for data science talent, especially in the Technology/IT and Industrial domain, can hardly catch up with the supply of skilled data scientists. That’s why it doesn’t come as a surprise that a significant part of the data scientists in India (more than 40%) have received a ‘data scientist’ job title within the last 24 months. So, becoming a data scientist in India seems like a truly golden opportunity. Let’s see what that means in terms of salaries! According to upgrad.com, “data scientist” is the highest paying job in India. To be more precise, the average data scientist salary listed on Glassdoor is above 1,000,000 rupees per year and it could go up to 2,000,000 rupees for more experienced candidates. Of course, corporations based in large cities like Mumbai and Bangalore offer higher salaries. But keep in mind that this may soon change. Many international companies prefer to open an office in Hyderabad, as it’s much more affordable. No wonder giants like Facebook, Microsoft, Google, Amazon, and P&G already call Hyderabad their home. And that translates into even more career opportunities for data scientists in India… The post How To Become A Data Scientist In India in 2020? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/2Nab10Q In these series of articles, we will provide you with a brief dictionary of terms surrounding data science including AI, machine learning, and deep learning. What is Data Storytelling in Data Science?Data storytelling is a methodology for communicating information, tailored to a specific audience, with a compelling narrative.It is the last ten feet of your data analysis and arguably the most important aspect. The rate that businesses collect data today is phenomenal. You can now collect data on every aspect of your business and, in fact, your life. Despite the surgence of solutions, such as BI tools, dashboards, and spreadsheets over the recent decades, businesses still are unable to fully take advantage of the opportunities hidden in their data. The last step of the data science process involves communicating potentially complex machine learning results to project stakeholders who are non-experts with data science. Data storytelling is an important skillset for all data scientists. The post What is Data Storytelling in Data Science in 2020? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/2N3r00t A Box and Whisker Plot (or Box Plot) is a convenient way of visually displaying the data distribution through their quartiles.The lines extending parallel from the boxes are known as the “whiskers”, which are used to indicate variability outside the upper and lower quartiles. Outliers are sometimes plotted as individual dots that are in-line with whiskers. Box Plots can be drawn either vertically or horizontally. Although Box Plots may seem primitive in comparison to a Histogram or Density Plot, they have the advantage of taking up less space, which is useful when comparing distributions between many groups or datasets. Here are the types of observations one can make from viewing a Box Plot:
Two of the most commonly used variation of Box Plot are: variable-width Box Plots and notched Box Plots. The post Data Visualization Explained – Box and Whisker Plot appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/2Y5S3yv Line Graphs are used to display quantitative values over a continuous interval or time period. A Line Graph is most frequently used to show trends and analyse how the data has changed over time.Line Graphs are drawn by first plotting data points on a Cartesian coordinate grid, then connecting a line between all of these points. Typically, the y-axis has a quantitative value, while the x-axis is a timescale or a sequence of intervals. Negative values can be displayed below the x-axis. The direction of the lines on the graph works as a nice metaphor for the data: an upward slope indicates where values have increased and a downward slope indicates where values have decreased. The line’s journey across the graph can create patterns that reveal trends in a dataset. When grouped with other lines, individual lines can be compared to one another. However, avoid using more than 3-4 lines per graph, as this makes the chart more cluttered and harder to read. A solution to this is to divide the chart into smaller multiples. The post What is a Line Graph in Data Visualization? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/2N178en Used to display the general levels of supply and demand of a particular asset by visualising the price actions through a series of line patterns. Kagi Charts are time-independent and help filter out the noise that can occur on other financial charts.This is so that important price movements are displayed more clearly. Recognising the patterns that occur in Kagi Charts is key to understanding them. While Kagi Charts do display dates or time on their x-axis, these are in fact markers for the key price action dates and are not part of a timescale. The y-axis on the right-hand side is used as the value scale. The line in a Kagi Chart initially moves vertically in the same direction of the price movement and will continue to extend, so long as the price, regardless of how small, maintains the same direction. Once the price hits a pre-determined “reversal” amount, the line makes a u-turn and goes in the opposite direction. So, each of the little horizontal lines on the chart indicates where a price reversal has taken place. When a horizontal line joins a rising line with a plunging line it’s known as a “shoulder”, while a horizontal line connecting a plunging line with a rising line is known as a “waist”. The varying thickness or colour of the line is dependent on the price behaviour. When the price goes higher than a previous “shoulder” reversal, the line becomes thicker (and/or green) and is known as a “Yang line”. This can be interpreted as an increase in demand over supply for the asset and as a bullish upward trend. Alternatively, when the price breaks below a previous “waist” reversal, the line becomes thinner (and/or red) and is known as a “Yin line”. This signifies an increase in supply over demand for the asset and as a bearish downward price trend. Traders use the shift from thin (Yin) to thick (Yang) lines (and vice versa) as signals to buy or sell an asset. A Yin to Yang shift indicates to buy, while a Yang to Yin shift indicates to sell. The post What is Kagi Chart in Data Visualization? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/2B5kRya Bar Chat is also known as Bar Graph or Column Graph.The classic Bar Chart uses either horizontal or vertical bars (column chart) to show discrete, numerical comparisons across categories. One axis of the chart shows the specific categories being compared and the other axis represents a discrete value scale. Bars Charts are distinguished from Histograms, as they do not display continuous developments over an interval. Bar Chart’s discrete data is categorical data and therefore answers the question of “how many?” in each category. One major flaw with Bar Charts is that labelling becomes problematic when there are a large number of bars. The post What is a Bar Chart in Data Visualization? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/2C6l7NX Area Graphs are Line Graphs but with the area below the line filled in with a certain colour or texture. Area Graphs are drawn by first plotting data points on a Cartesian coordinate grid, joining a line between the points and finally filling in the space below the completed line. Like Line Graphs, Area Graphs are used to display the development of quantitative values over an interval or time period. They are most commonly used to show trends, rather than convey specific values. Two popular variations of Area Graphs are: grouped and Stacked Area Graphs. Grouped Area Graphs start from the same zero axis, while Stacked Area Graphs have each data series start from the point left by the previous data series. The post Area Graph in Data Visualization appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/2MVb3cH |