In this post, we’re going to discuss ten essential things that you must understand to excel in statistics. These include concepts, equations, and theorems that will not only greatly help you pursue data science but prove your understanding of statistics as well.

1. Sample

A sample refers to a smaller, specific group of an entire population. For instance, if we consider “all the countries in the world” as a population, a sample could be “countries that have published data on unemployment since 2000”. The objective of studying a well-chosen sample is to test hypotheses or make inferences about population data in a valid and reliable way.

2. Population

In statistics, a population stands for the entire group that you’re interested in studying. Note that, here, a population doesn’t necessarily refer to people always. Instead, it can be a group comprising any elements that you’re looking to study — from species, countries, and objects to organizations, events, and more. Therefore, the term “population” appears with a slightly different explanation from what we commonly understand.

3. Measures of central tendency

A measure of central tendency refers to a summary statistic, which represents a dataset’s center point. It’s the single value that you can use to describe the entire dataset by identifying the central position of the dataset. In statistics, three measures of central tendency are most commonly used — mean, median, and mode.

Mean

Mean is one of the most popular methods used in the calculation of central tendency. To calculate the mean, you need to add up all the values in your dataset and divide the sum by the dataset’s number of values. This method is generally considered the arithmetic mean. The other two methods of mean used to locate the central tendency include geometric mean and harmonic mean.

Median

You can think of median as the middle value of your dataset. Here, the dataset is arranged in the order of magnitude. The method for identifying the median slightly differs depending on whether you’ve got an even number or an odd number of values in the dataset.

If your dataset has an even number of values, you need to add the two middle values and calculate the average to identify the median of your dataset.

If your dataset has an odd number of values, the median would be the middle mark of it.

Mode

It’s the score that most frequently occurs in your dataset. If your dataset has multiple values, which most frequently occur, it contains multiple modes. However, in some cases, there may not be any mode at all. For instance, if the dataset has continuous data, there may not be any value occurring more frequently than another.

4. Measures of dispersion

You use a measure of dispersion to describe the variability in a population or sample. It’s commonly used together with one of the measures of central tendency to obtain an overall description of a dataset. In statistics, two key types of dispersion methods are used — absolute measure of dispersion and relative measure of dispersion.

Absolute measure of dispersion

It comprises the same unit as your original dataset. The variations with regard to the average of deviations such as means or standard deviations are expressed using this method. Different types of absolute measures of dispersion include Range, Quartile Deviation, Interquartile Range, Standard Deviation, and Mean Deviation.

Relative measure of dispersion

You use a relative measure of dispersion to perform a comparison between two or more datasets. Here, values are compared without units. Relative measures of dispersion that are commonly used include the following.

• Coefficient of Variation
• Coefficient of Standard Deviation
• Coefficient of Range
• Coefficient of Mean Deviation
• Coefficient of Quartile Deviation

5. Frequentist approach

The frequentist approach is used to make hypotheses on the underlying truths of repeatable experiments. It calculates the frequency of repeatable experiments in the long run. To understand this clearly, let’s consider an example of a coin toss. If the probability of a fair coin landing heads is 0.5, it means that if it’s tossed enough times, we can expect to see heads at 50% of the total number of tosses. One common drawback of the frequentist approach is the result of the experiment depends on how many times you repeat it. Therefore, if an event isn’t repeatable, you cannot actually define its probability using the frequentist approach.

6. Bayesian approach

The Bayesian approach applies probability to statistical problems. It provides mathematical tools that we can use to update our beliefs regarding random occurrences when new data about those occurrences are brought to light. There’s a major difference between the frequentist approach and the Bayesian approach. In the former, we try to eliminate uncertainty by figuring out estimates while in the latter, we try to preserve and perfect uncertainty by altering our beliefs by considering new data or evidence. To be able to apply the Bayesian approach successfully to a problem, you must become familiar with two major concepts — Bayes Theorem and Conditional Probability.

7. Central Limit Theorem

In modern statistics, the central limit theorem or CLT is one of the most important theories used in hypothesis testing. It expresses that as the sample size gets larger, the distribution of the mean concerning a variable will be like a normal distribution irrespective of the distribution of that variable in the population. Note that the central limit theorem definition mentions the sampling distribution will start becoming a normal distribution when the sample size is sufficiently large. Though it depends on the distribution of the variable in the population, a sample size of 30 is typically considered sufficient for the majority of distributions. Note that you may need to have larger sample sizes for strongly asymmetric distributions.

8. Law of Large Numbers

The law of large numbers is one of the key theorems in probability and statistics. It states that as you repeat an experiment multiple times and calculate the sample mean, the result will better approximate the expected or true value. There’re two versions of the law of large numbers — the strong law and the weak law. The strong law of large numbers gives a result, which’s almost accurate to your expected mean. The weak law of large numbers focuses on convergence in probability and the result is close to the expected outcomes but not as accurate as the result of the strong law.

9. Sample Representativeness

You can consider a representative sample as one that represents your population accurately, meaning the sample matches some of the characteristics of the population. Note that a representative sample needs to be an unbiased representation of your population. You can use different ways to evaluate representativeness — from gender, age, education, and profession to socioeconomic status, chronic illness, and more. However, it greatly depends on the scope of your study, the available information about your population, and how detailed you’re looking to get. If the statistic, which you obtained from sampling, doesn’t represent the parameters of your population, it’s called an unrepresentative sample. Therefore, you must try to avoid selection bias in order to achieve randomness.

10. Hypothesis Testing

In statistics, hypothesis testing refers to the method that statisticians use to make statistical decisions with the help of experimental data. Basically, the result of hypothesis testing lets you interpret whether your assumptions have been violated or they hold. If you figure out that the assumptions have been violated, the experiment will be of little use and might not be repeatable.

Here’re the key steps in hypothesis testing.

• Specify the null hypothesis
• Specify the significance level (known as the a level also)
• Compute the probability value (known as the p value as well)
• Compare the probability value with the significance level

If you see the probability value is lower, you should reject the null hypothesis. If it’s higher than the conventional significance level of 0.05, you may consider the findings inconclusive. Typically, statisticians design experiments for allowing the null hypothesis to get refuted. However, failure to refute the null hypothesis doesn’t establish support for it. It only means that you don’t have sufficiently strong data for refuting it.

Wrapping up

We hope that this article has been insightful. All of the above topics are pivotal for developing a good understanding of the fundamentals of statistics. However, this isn’t a comprehensive list of the topics that you need to focus on solely. If you want to develop a deeper understanding of these concepts along with other important ones, you should consider joining a course on statistics.

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To learn more about variance and bias, click here and read our another article.

The post Ten Things That You Need To Know In Statistics: The Fundamentals of Statistics first appeared on Magnimind Academy.

Acquiring the skills required to become a data scientist is extremely essential for anyone looking to get a job in that position. But you should understand that data science is a highly complicated field and it requires a lot of skills to become a data scientist. While it’s practically impossible for anyone to have all the skills related to data science field, there’re some skills that differentiate between a good data scientist and a great data scientist. Here, we’re going to discuss the skills which you should focus upon to become a top-notch data scientist.

Crucial skills to become a top-notch data scientist

CONTINUED EDUCATION

While there’re notable exceptions, in general, data scientists come with a strong educational background that helps them to attain the in-depth knowledge necessary to perform their job responsibilities. Forty-six percent of them come with PhDs while eighty-eight percent hold a Master’s degree at least. The most common fields of study include mathematics, statistics, computer science, and engineering. If you want to become a top-notch data scientist, your education shouldn’t end there. You should try to undertake online training to learn specialized skills that are creating buzz around the data science domain. Then, you can go ahead to get a Master’s degree in any of the fields related to data science. In addition, you should keep on practicing what you learned in a class by exploring data analysis, starting a blog etc to learn more about the topics.

PYTHON

Python is the most popular programming language in the data science field. In fact, a large percentage of data scientists prefer to use Python as their primary programming language. In data science processes, it can be used for almost every step involved. You can use not only across large datasets but in creating datasets as well. A huge percentage of data scientists across the globe consider Python as the foundation for performing data analysis tasks. Hence, to become a top-notch data scientist, you should try to become a master of this language.

R

R programming language is heavily used in data science for statistical problem-solving. You can use this language to solve almost any data science related problem and thus, attaining a solid understanding of R is crucial to become a top-notch data scientist. Though R comes with a steep learning curve, there’re lots of great resources available on the web that can help gain adequate knowledge. Alternatively, you can join a coding bootcamp as well to gain knowledge and hands-on experience.

SQL

Even though Hadoop and NoSQL have become a large part of data science, proficiency in SQL (Structured Query Language) is important to become a top-notch data scientist. SQL is particularly designed to help data scientists to access, communicate as well as work on data. It can also help in transforming database structures and carrying out analytical functions. Concise commands of SQL can not only help to save time but also lessen the amount of programming he/she needs to perform difficult queries.

APACHE SPARK

One of the most widely used big data technologies, Apache Spark is a big data computation framework similar to Hadoop. However, Spark is faster than Hadoop which reads and writes to disk, but Spark caches the computations in memory. To become a top-notch data scientist, you need to become proficient in Apache Spark as it’s particularly designed for data science to help in running complicated algorithm faster. It helps in distributing data processing when you’re dealing with a massive amount of time and thus, saves you time. In addition, it helps data scientists to deal with complex unstructured datasets and it can be used on a single machine or cluster of machines. The strength of Spark lies in its platform and speed, both of which contribute heavily toward carrying out data science projects easily.

HADOOP PLATFORM

Having experience with Pig or Hive is considered a strong selling point and thus, important to become a top-notch data scientist. Data scientists may encounter situations where they need to send data to other machines or the volume of the data they’ve exceeds the memory of the system, this is where Hadoop helps them. Hadoop can be used to convey data to different points on a system quickly. In addition, it can be used for data filtration, data exploration, data sampling, and summarization.

MACHINE LEARNING

Though a significant number of data scientists aren’t proficient in areas and techniques of machine learning, a solid understanding of them is needed to become a top-notch data scientist. Machine learning techniques like logistic regression, decision trees etc help one to solve various data science problems which are based on predictions of key outcomes. Advance machine learning skills like learning methods (supervised learning, unsupervised learning, and reinforcement learning), natural language processing, time series, computer vision, adversarial learning etc can help a data scientist stand out of the pack.

UNSTRUCTURED DATA

Ability to work efficiently with unstructured data is crucial to become a top-notch data scientist. Unstructured data refers to undefined content which doesn’t fit into database tables. These include blog posts, videos, customer reviews, video feeds, social media posts etc which are heavy texts lumped together. Sorting unstructured data is difficult as they’re not streamlined. By working with unstructured data, data scientists can untangle insights which can help in effective decision making.

DATA VISUALIZATION

A huge amount of data is being generated frequently by the business world and this data has to be translated into a format which will be easy to understand by average people. As people understand pictures in forms of graphs and charts more than raw data naturally, it’s the responsibility of a data scientist to visualize that data with the help of different data visualization tools like Tableau, Matplotlib, ggplot etc. These tools help data scientists to convert complicated results from their projects to an easily comprehensible format. Data visualization enables businesses to directly work with data. This lets them grasp insights quickly and act on business opportunities to gain a competitive advantage.

BUSINESS ACUMEN

A robust understanding of the particular industry you’re working in is crucial to become a top-notch data scientist. It’s also important to be able to discern the problems critical for the business and identify new ways the company should be leveraging the captured data. To perform this task efficiently, data scientists need to understand how the problems they solve can impact the business.

COMMUNICATION SKILLS

When it comes to hiring an elite data scientist, companies look for someone who can fluently and clearly communicate their findings to non-technical teams like Sales or Marketing, apart from having the above skills. A data scientist has to enable the business to make useful decisions by arming it with quantified insights. He/she also needs to understand the requirements of the non-technical teams in order to appropriately wrangle the data. Effective data storytelling is another key requirement to become a top-notch data scientist. A data scientist must know how to develop a storyline around their findings to make it simple for everyone to understand. For example, presenting a table of data isn’t as sharing the findings from that data in a storytelling format.

Final Takeaway

These days, there’re lots of events, coding seminars, data science meets, hackathons etc organized by leading organizations to groom talents and scout for the best as well. Participation in those events not only helps you to broaden your knowledge horizon to encounter real-world challenges but also to network easily. Workshops and data science bootcamps greatly help you in taking your skills to the next level and give you a competitive edge as well. You need to have a solid understanding of the majority of the above skills to become a top-notch data scientist. And to learn and sharpen those skills, you need to pick a premier institute which offers best courses on data science topics. These days, the marketplace is flooded with lots of data science courses. A significant number of training academies offer lucrative discounts on those courses. However, it’s much more than pretty packages or hefty discounts to choose the right course for yourself. In order to succeed in your journey to become a top-notch data scientist, you should have a basic knowledge of the courses you’re planning to undergo, their individual offerings to be able to compare them and take your pick, and a clearly chalked out career plan.

The post What Does It Take To Become A Top-notch Data Scientist? first appeared on Magnimind Academy.

If you step back a couple of years ago, you will find that the predominant focus was on retrieving precious insights from data. As companies and organizations started making data-based and data-driven decisions, which brought several benefits their way, the significance of data management started to sink in the industry – slowly but surely. This also made the interested parties realize that the quality of data was important to derive useful insights because it’s the principle of “Garbage In, Garbage Out” that works in the domain of data science too. Even if you are capable of creating the best models, your results are likely to be weak and ineffective in case your data isn’t qualitative. And this was what brought the role of the data engineer under the spotlight.

According to Gartner, merely 15% of big data projects ever make their way into production. According to domain experts, one of the chief reasons behind such failures is due to the inability to build a production pipeline, which is one of the principal tasks of a data engineer. In the modern age of analytics, data scientists get most of the spotlight and attention. However, the roles played by data engineers are equally important, though they are often overlooked. It’s important to realize that data science (and even data analytics) would fail to flourish if no data engineering workbench exists. If you don’t believe it, you can consider what Glassdoor’s records say.

According to Glassdoor’s data in 2018, the number of job openings earmarked for data engineers was almost five times more than that for data scientists. Elsewhere, one may find data scientist jobs exceeding the number of data engineer jobs though some say it could be because numerous organizations don’t always (or are unable to) draw a distinct line between a data scientist and a data engineer. Thus, they end up posting jobs for the former whereas in reality, the jobs should have been seeking data engineers instead. Such actions on the part of organizations are perhaps triggered by their ignorance of the significant differences between data scientists and data engineers. Many reports have revealed that the majority of organizations require more data engineers than data scientists on their team. So, the question comes to this – what exactly is data engineering and how’s the role played by a data engineer different from that played by a data scientist.

Let’s dig a little deeper to answer the questions and find out the differences between data scientists and data engineers.

1- Who is a data engineer?

S/He is a professional with specialized skills in creating software solutions around Big Data.

Another way of defining a data engineer is that s/he is an inquisitive, skilled problem-solver, who loves both data and creating things that are useful to others. Thus, along with data scientists and business analysts, data engineers form an integral part of the team effort that converts raw data in ways which offer organizations useful insights and provides them with the much need competitive edge.

To understand what the role of a data engineer is, it can be said that this professional is someone who builds, develops, evaluates and maintains architectures like databases and large-scale processing systems. In contrast, a data scientist is someone who cleans, organizes, and acts upon (Big) data.

It’s the job of data engineers to suggest and at times, even implement ways to improve data quality, efficiency, and reliability. To handle such tasks, they need to utilize a range of tools and languages to blend systems together or try to track down opportunities to get hold of new data from other systems, which can help system-specific codes, for example, to act as the basic information in advanced processing by data scientists.

A data engineer will also need to make sure that the architecture that’s in place is capable of supporting the needs of the data scientists as well as the business/organization and its stakeholders.

In order to deliver the required data to the data science team, it will be the responsibility of the data engineers to develop data set processes for data mining, modeling, and production.

2- Key differences between data scientists and data engineers

With respect to skills and responsibilities, you’ll find considerable overlapping between data scientists and data engineers. One of the key differences between data scientists and data engineers is the area of focus. For data engineers, the emphasis is on creating architecture and infrastructure for data generation.  On the contrary, the focus of data scientists is on advanced statistical and mathematics analysis on that generated data.

Though the role of data scientists demands a constant interaction with the data infrastructure that the data engineers have created and maintained, the former isn’t responsible for that infrastructure’s creation and maintenance. Rather, they can be called the internal clients, whose job is to perform high-level business and market operation research to spot trends and relations, which in turn need them to use an array of sophisticated methods and machines to interact with the data and act upon it.

It’s the job of data engineers to provide the necessary tools and infrastructure to support data analysts and data scientists so that these professionals can deliver end-to-end solutions for business problems. Data engineers are tasked with creating high performance, scalable infrastructure that helps deliver business insights with clarity from raw data sources in addition to implementing complex analytical projects where the emphasis is on gathering, evaluating, managing, and visualizing data along with developing real-time and batch analytical solutions.

Perhaps you now understand that despite some key differences between data scientists and data engineers, the formers depend on the latter. While data scientists deal with advanced analysis tools like Hadoop, R, advanced statistical modeling, and SPSS, the focus of data engineers remain on the products that support such tools. Thus, a data engineer may deal with NoSQL, MySQL, SQL, Cassandra, etc.

In a way, you can say that in the data value-production chain, the role of data engineers is akin to the plumbers since they facilitate the job of data scientists, data analysts and other professionals working on the fed of data science. As with any infrastructure, plumbers don’t get the limelight, and yet, they are irreplaceable since nobody can get any work done without them. The same applies to data engineers as well.

3- Language, tools, and software used by data engineers

Due to the difference in their skill sets, differences between data scientists and data engineers translate into the use of different tools, languages, and software use.

For data scientists, common languages in use are Python, R, SPSS, Stata, SAS, and Julia to construct models. However, Python and R are the most popular tools without a doubt. When these data science professionals are working with Python and R, they often resort to packages like ggplot2 to make remarkable data visualizations in R or opt for the Pandas (Python data manipulation library). There are several other packages that can come for them, which include NumPy, Scikit-Learn, Stats models, Matplotlib, etc. The data scientist’s toolbox is also likely to have other tools like Matlab, Rapidminer, Gephi, Excel, etc.

The tools that data engineers often work with include Oracle, SAP, Redis, Cassandra, MongoDB, MySQL, PostgreSQL, Riak, neo4j, Sqoop, and Hive.

Languages, tools, and software that both the parties have in common are Java, Scala, and C#.

One of the key differences between data scientists and data engineers emerges from the emphasis given on data visualization and storytelling, which gets reflected in the tools these professionals put to use, some of which are mentioned above.

4- When organizations get the roles wrong

As mentioned before, several organizations fail to distinguish the key differences between data scientists and data engineers and often task the former with the job that the later is specialized to do. For example, asking data scientists to create a data pipeline, which is the job of a data engineer, would mean making the former function at just 20-30% of their actual efficiency. So, it becomes important to know the differences between data scientists and data engineers and hire each for roles specifically designed to match their skill sets.

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To learn more about data science, click here and read our another article.

The post What Are The Differences Between Data Scientists And Data Engineers? first appeared on Magnimind Academy.

1- What is data science?

The world is becoming a digital space day-by-day and businesses across the globe are dealing with massive amounts of data every day. Businesses have started to realize that this massive amount of data is of no use until they become able to use it to their advantage. Data science is the field that offers a perfect blend of various high-end tools and technologies that help businesses discover patterns hidden in that data. So, you can consider data science as a field with the objective of making business predictions and decisions by analyzing massive amounts of data.

The entire data science process can be distributed in the following stages:

• Data capture
• Maintenance of data
• Processing of data
• Data analyzing
• Communication of the findings

2- Components of data science

When it comes to demystifying data science, it’s important to understand the key components of data science. Let’s have a look.

• Various types of raw datasets
• Computer programming language
• Statistics and probability
• Machine learning
• Big data

3- What is a data scientist?

Probably you’ve already heard that the job role of data scientist has been declared as the hottest one of the 21^st century. These days, data scientists have become crucial assets for almost any organization. These professionals are data-driven individuals with robust technical skills and are capable of working with large amounts of information to derive actionable insights that help to build business strategies in their companies.

4- Different roles in the data science field

Over the last decade, a variety of job positions associated with data science have emerged. Some of the most common ones include data scientist, data analyst, and data engineer. Let’s have a quick look at the skills required to get into these positions to help you chalk out your future career path.

• Data scientist: Strong mathematical and statistical skills, programming skills, data visualization, storytelling, among others.
• Data analyst: Mathematical and statistical skills, programming skills, data wrangling, data visualization, among others.
• Data engineer: Databases, programming languages, frameworks, among others.

Final Takeaway

If you’re interested to step in the data science field, it’s important to understand that to become a successful professional in this field, you’d need to acquire a diverse skillset. This is an ever-evolving field and it’s not possible to learn everything. But, if you can master the basics, you’ll surely be in a position to learn anything as and when required. And that’s probably the best way to succeed in fast-developing field like data science.

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To learn more about data science, click here and read our another article.

The post Demystifying Data Science first appeared on Magnimind Academy.