The post What is HDFS? An Introduction to HDFS appeared first on AcadGild.

]]>HDFS or Hadoop Distributed File System is one of the most basic components of the big data framework that is Hadoop. HDFS is cutting edge owing to its capability of storing and retrieving multiple files at the same time, all at extremely high speeds.

HDFS architecture ensures that it can run on commodity hardware so that it can process a large amount of unstructured data. Owing to this feature, it is incredibly fault-tolerant – identical copies of the data are stored at multiple locations within the hardware, and an inability to retrieve data from one location does not cripple the system. The same data can be extracted from other locations quickly.

HDFS, when used, improves the data management layer in a huge manner. The cluster is, therefore, able to manage a large amount of data concurrently, thus increasing the speed of the system. HDFS is also storing terabytes and petabytes of data, which is a prerequisite in order to analyse such large amounts of data properly.

Operator intervention is not required in most cases, as HDFS can easily manage multiple thousands of nodes quickly and efficiently. The architecture of the system uses the best of both worlds – distributed and parallel computing – at the same time, so as to run the system at a quicker pace compared to other systems. HDFS also has another important feature called the capability to rollback. This means that the system is allowed to return back to its previous version even after updates are carried out. This is extremely useful especially since there could be many bugs that could cripple the system in beta updates. However, the highlight of HDFS is that the integrity of the data is maintained at all times, and the stored data is virtually incorruptible. This, as mentioned earlier, is because the system stores data at multiple locations so that it is never lost because of any software or hardware issues. Therefore, HDFS has high degrees of reliability and efficiency compared to the existing systems.

The obvious reason is the safety that the integrity of data is maintained always. The data is stored in three sets of identical copies at multiple locations, thus ensuring that your data is never wiped out regardless of any accidents or bugs. If the data you get is in the streaming format, HDFS is perfect for that. This means that the system is more suited for applications which require batch processing, rather than interactive ones. HDFS works better for data with high throughput rather than low latency.

The sheer size of the amount of data than HDFS can work with is one important reason why you should implement it. the system is capable of working with extremely large sets of data, which could range into the terabytes. Tens of millions of files can be supported in a moment, in the system. The aggregate data bandwidth of HDFS is extremely high, and the focus is always on scaling out with ease.

Another advantage of HDFS is that it is extremely portable, which will be required for most large organisations today. It can work on many types of commodity hardware with ease, without any problems associated with compatibility.

If you are looking to get into data analytics, starting with Hadoop would be ideal. You can check out the courses on big data at Acadgild, for more information!

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]]>The post Top 8 Big Data Analysis Tools That Every Data Analyst Must Know appeared first on AcadGild.

]]>R is currently one of the most popular analytics tools for data scientists. It has outperformed SAS in its use and has now became a priority tool even for big organizations that can very easily deploy paid enterprise tools like SAS. In recent years R has turned into a significantly strong platform. It can handle vast data sets and is also amazingly flexible.

The R language is broadly utilized tool among data miners for creating the data analysis and statistical program. Other than data mining it offers graphical and statistical procedures, including nonlinear and linear modeling, standard statistical tests, time-series analysis, clustering, classification, and so on.

Apache Spark is one of the most incredible open source big data tools. Apache Spark was designed to fill the shortcomings of Hadoop especially related to data processing. Spark is many times faster than Hadoop in data processing. Spark’s fast data processing speed is due it’s in memory processing as compared to Hadoop’s traditional in disk processing. It also has built-in APIs for Python, Java, or Scala.

Zoho Analytics offers diverse reporting features, including KPI widgets, tabular view components, and pivot tables, enabling it to produce reports that offer great insights. The Zoho analytics platform allows collaborative analysis and review, allowing the clients to work with co-workers on report improvement and central leadership. The platform offer extremely easy to use – drag and drop visual interface – which means users don’t need coding experience with Zoho analytics.

Tableau Is a data analysis and visualization tool which can connect with many data sources comfortably. The big advantage with Tableau is its ability to create interactive dashboards. These dashboards can be created without much coding knowledge, through visual intuitive drag and drop interface. Tableau has been very popular among organizations due to its ability to translate data into insightful visual dashboard. Lastly, Tableau utilizes application integration innovations like JavaScript APIs and single sign-on application to include Tableau analytics into basic business applications consistently.

Hadoop is simply the most widely used data analytics tool currently in the industry. Hadoop is completely open source data processing tool which offers processing and storage of big data. Hadoop is extremely flexible in processing big data – as it can handle structured as well unstructured data.

Mongo DB is a flexible NoSQL based database system. This is one of the good resource for data which isn’t structured. The top features of Mongo DB are :

- Load balancing – Mongo DB offers a database platform which can run smoothly even when load is high as it can run over multiple servers while balancing the load among them.
- Ad hoc query – Mongo DB supports ad hoc queries such as field or range query.
- File storage system – Mongo DB is also being widely used as an efficient file storage system which can store files across multiple servers while using load balancing feature.

It’s a drag and drop chart production cloud based tool that can function well on a computer or tablet. Chart.io can connect with various types of data sources and databases, ranging from MySQL to Oracle. Data can be sourced and integrated from various sources with a single tap of a button before performing the analysis. It can create an assortment of charts, graphs for example, pie charts, bar graphs, scatter plots etc. Chart.io is extremely popular among marketers for performing marketing analytics.

KNIME enables you to control, analyze, and to demonstrate data in an unimaginably intuitive mode with the help of visual programming. KNIME can be used to run python, R, chemistry data, text mining, and others, which gives you the alternative to dabble with the further developed code driven analysis. The tool is finding its use in customer data analytics, pharma research, financial data analytics. KNIME is fast becoming an open source alternative to SAS.

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]]>The post What is Data Mining and How is it done? appeared first on AcadGild.

]]>Data mining is the process of analyzing the data in order to find hidden patterns and systematic relationships. These relationships and patterns are then used to predict the future behaviors. Data mining finds insights from the huge amount of structured and unstructured data that help businesses make more fact-based decisions. Even though the term data mining is a relatively new term, the practice itself is not. Companies have been using data mining techniques such as supermarket scanners that track customer purchases. With the advent of Big Data and advancement in computer technology, Data Mining grew more prevalent and feasible. Various techniques and tools are used to mine data. Each tool consists of its own peculiarities and merits. The tools are selected according to the requirement.

For quicker analysis of data, it is important to use the proper tool for the requirement. Following are the few essential tools used for Data Mining

**Rapid Miner –** It a very popular open source software that requires no programming to operate. It provides multifaceted data functions such as data pre-processing, predictive analysis and visualization.

**Weka –** It is a set of machine learning algorithms for data mining. They are either applied directly or called through a Java code. This tool is used to perform data pr-processing, clustering, regression, visualization and classification in data mining.

**Orange –** It is a Python library that powers Python scripts with Machine Learning and Data Mining algorithms. It is used for classifying, pre-processing, modelling, clustering and other miscellaneous functions.

**R –** It is an opensource software environment widely used for data mining tasks. It comes with huge community support as well as hundreds of libraries specifically built for data mining.

**Knime – **This tool is primarily used for data pre-processing in data mining. Data pre-processing means extraction, transformation and loading of data. Knime is very popular among the financial data analysts.

Data Mining and Data science may go hand in hand when it comes to data. But basically, they are two completely different things. Data science is a field of study which constitutes everything from Data Mining, Data Visualization, mathematics and Big data Analysis. It is now considered as the fourth paradigm of science after Theoretical, Empirical and computational science. On the other hand, Data Mining is a technique that finds trends in a data set. Clearly, Data mining is a subset of data science with many applications in the current data-driven world.

Data Mining Analysts plays a large role in realizing the business intelligence of organisations. They provide actionable ideas by analyzing the data. So, Data Mining Analyst is an important person in every industry. The average annual salary for a Data Mining Analyst in India is above â‚18 Lakhs. With more and more organisations trying to make use of data, the demand for qualified Data Mining Analyst is expected to go higher.

A smart step towards a successful career in Data Mining is to gain in-depth knowledge not just in the mining tools but also in the area of statistical methods and predictive models that support business needs. Visit Acadgild for great courses on Data Mining.

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]]>The post Importance of Statistics for Data Science | Statistics and Data Science appeared first on AcadGild.

]]>The main aim of data science is to analyse the unstructured data being produced today, but this is often impossible to do qualitatively – it has to be done quantitatively. After analyzing this data, organisations need to obtain real insights about their customers and their needs, so that these insights can be translated into proper business value quickly. Therefore, the onus is on data scientists to carry out their analyses properly, so as to improve and optimise the way business is conducted. Organisations in a variety of fields, ranging from health care to entertainment currently follow this model.

Data scientists must have a deep understanding of statistical concepts in order to carry out quantitative analysis on the available data. Therefore, they must learn statistics for data science to be successful – this is a given. However, there are a lot of statistics for data science tutorials available online, and the ones by Acadgild are comprehensive enough to provide you with a thorough understanding of what is discussed here.

Let us take a look at some statistical concepts that every data scientist must know, to make his job easier.

Linear regression is a lynch-pin of statistics and is used to predict the value of a variable based on the values of the other variables present in the analysis. This is done by fitting the best linear relationship in the scatter-plot of the values of two variables – the dependent and the independent ones. The best fit is obtained by ensuring that the sum of all distances between the obtained shape and the values of each point is as less as possible.

There are two types of linear regressions – simple and multiple. In the former, there are only two variables used – a dependent one and an independent one. In the latter, more than one independent variable is used in a bid to predict the value of the dependent variable more accurately.

This is a general term, which is used to refer to data mining methods which categorize the available data to obtain correct and accurate analysis and predictions from them. It is also called a Decision Tree, and there are two main classification methods – Logistic Regression and Discriminant Analysis. For more information on these complex statistical methods, you should check out the course by Acadgild.

In this method, samples are drawn from the original data samples repeatedly to obtain a unique sampling distribution which follows the actual data set. This is usually done when the data set is far too large to be analysed entirely, as is the case in most big data analysis. The estimates obtained from this method is unbiased, as it is from the unbiased samples which are from all possible results of the data that the researcher has.

In order to learn more about basic statistics for data science, the best thing to do would be to enroll for an online course and complete it. Acadgild offers high quality and highly rated courses which can put you on your way to a successful career as a data scientist.

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]]>The post Understanding the Need for Hypothesis Testing Statistics appeared first on AcadGild.

]]>Hypothesis testing statistics is an important tool using which statistical decisions are made using experimental data. The concept of hypothesis testing is based on the assumption of a population parameter. We often might need to make statistical decisions based on a given hypothesis. These decisions include various scenarios like accepting or rejecting a null hypothesis. Each hypothesis testing test results in significant value for the particular test. If the significance value of a particular test is relatively higher than the predetermined significance level, then a null hypothesis is accepted in a hypothesis test. The null hypothesis is rejected if it is vice-versa. You should know how to calculate hypothesis testing statistics for being proficient in the field of data science and to master hypothesis testing.

Different statistical terms like null hypothesis, type 1 error, level of significance, type 2 error, two-tailed test and one-tailed test are all examined and explained in hypothesis testing courses. Learning these terms will help you in eventually conducting a hypothesis test efficiently. Your statistics courses will help you ascertain the importance of these terms used in hypothesis testing.

Hypothesis testing becomes a vital part of statistics to analyse two exclusive statements regarding a population and to determine which statement is closer to the sample data provided. In other words, hypothesis testing is about trying to find out how likely is the observation of a phenomenon to have occurred based on statistics. Confirmatory data analysis is the other term used to define statistical hypothesis testing activity. A hypothesis test may return a p-value which is used to quantify the result of the test performed.

It is vital for you to have the knowledge of hypothesis testing as it gives you an ascertainment of the idea that if a popular phenomenon has occurred or not. Simply to find out if your data has some statistical significance or not you need hypothesis testing. Various courses are available to make you more competent in statistics by throwing light on statistical inference hypothesis testing and testing of hypothesis in statistics with examples. An understanding of statistical hypothesis is necessary for quantifying your answers for all the questions on the sample data collected. You can begin to make claims regarding your assumptions only when you interpret the statistical hypothesis test results.

Only data may not be interesting what makes it interesting is the right interpretation of data using statistical tools. To arrive at a conclusion or decision about the likelihood of a chance of the collected statistical data an in-depth knowledge about hypothesis testing is essential. Though computer programming languages are essentially a vital part of technological developments knowledge about statistical tools like hypothesis testing will help in producing productive conclusions. Why wait for tomorrow, when you can join hypothesis testing in statistics courses at Acadgild right now.

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]]>The post Cumulative Distribution Function Explained in Detail appeared first on AcadGild.

]]>Practical tools are used to enhance the profitability of business today. A data scientist certainly is more well-versed in statistics than a software engineer. There are different statistical data used by data scientists for getting an edge in business. Statistics in data sciences is used to increase the profits of a business by cutting down the cost in some way or the other.

To find out some quick pointers that will help you understand the important concepts integrated with statistics, read on.

An integral concept of Probability Distribution Function (PDF) is the cumulative distribution function (CDF). A common aspect of PDF and CDF is that both of them are used to represent the random variables. Just like the basics of a probability density function, probability mass function and Bernoulli distribution data scientist needs the understanding of cumulative frequency distribution. A CDF is used to ascertain the probability of a random variable that is less than a certain value.

To ascertain the concept of cumulative frequency distribution better we need to know about the different types of data. There are apparently two types of data such as discrete variables and continuous variables. The discrete variables are those that have a set of finite variables. For example, you cannot have the 3,34567 medical procedures as it would be misleading. The number of medical procedures, in this case, can be either 3 or 4.

Whereas, a continuous variable cannot be listed like a discrete variable. But these have to be referred to by a formula as there can be an infinite number of continuous variables. An example to understand continuous variables better would be your age say you are 35 years old. You cannot be exactly 35 but 35 years, 210 days, 2 hours, 25 seconds and so on. Different probability distribution techniques are used for calculating with discrete and continuous variables.

A cumulative probability is represented by a graph of the cumulative distribution function. Therefore, when we take cumulative distribution function example as a six-sided die, the cumulative distribution function for it will look like a staircase. Every step moving upward will have the value 1/6 plus the value of the previous probability. At the end of the graph in this case at the six-step, it will be 100%.

The cumulative distribution function is one of the basic tools of statistics essentially required by the data scientists to ace the job. To figure out the concepts running underneath the hood of data sciences concepts like cumulative distribution frequency are a must.

You may sometimes use only Python or Oracle programs to solve your issues but having an in-depth knowledge of statistics will give you and your team a better approach towards the solution. Learn more about CDF and move towards achieving the organisational goals better and faster. So, stop thinking and join the statistics courses on cumulative distribution frequency in Acadgild for easy manipulation and abstraction of data.

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]]>The post Probability Distribution Explained appeared first on AcadGild.

]]>Probability distribution gives the probability of an event that is likely to occur in a given set of circumstances. A probability distribution can be explained with formulas or plotted through graphs for easy interpretation of the data. It is the most common way of describing the probability of an event. A probability distribution function may be any function used to define a specific probability distribution. A probability distribution table is a result of equations that connects every outcome of an event with its probability of occurrence. A mean of the probability distribution is depicted by the average value of the variables in the particular distribution. Mean, median and mode are the vital part of the probability distribution.

Before digging deep into the different types of probability distribution let us know about the types of variables used in these distributions. Data can be either discrete or continuous in nature. Discrete variables are those that have an outcome out of a specific set of variables. A simple example is a six-faced die when you roll the die the possible outcomes are 1, 2, 3, 4, 5 or 6.

Whereas continuous data may take up any value out of the given range. Here the given range may either be finite or infinite. Example of continuous data is the height of a girl which may be 4.5 feet.

Heading towards one of the easiest probability distribution that is Bernoulli distribution.

Here the outcome has only two possible ways. The two possible outcomes are success or failure and are denoted by 1 or 0 respectively. Which essentially means to say that a random variable X may be a success if takes the value 1 or failure if it takes the value 0. Here the probability of success and failure may not be the same.

To understand uniform distribution better let us get back to the rolling of a die example wherein the possible outcomes are equally likely to appear than the other. This type of probability distribution is deemed to be a uniform distribution.

A binomial distribution is a type of probability distribution where only two possible outcomes are probably success or failure, win or lose and more. Here the probability of both the outcomes is the same for all the trials.

A normal distribution is symmetric above the mean which means that the data near the mean is more likely to occur as opposed to the data that is far from the mean.

For events occurring at random point of time and the matter of interest is the number of times an event has occurred Poisson distribution is used.

The exponential distribution is highly used for survival analysis purposes. An example of exponential distribution is the lifespan of a machine.

As we know data science is a vast subject of analyzing data, statistics is an important tool or an essential component used by data scientists for arriving at a conclusion. The occurrence of the probability distribution is evident in many events of life, and hence it becomes a mandate to understand types of probability distribution for a data scientist.

Learn more about the widespread application of probability distribution by joining the best of Acadgild’s courses.

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]]>The post What Is Probability Mass Function With Example appeared first on AcadGild.

]]>A Probability Mass Function is also termed as a frequency function and is a vital part of statistics. Probability Mass Function integrates that any given variable has the probability that the random number will be equal to that variable. All the probabilities for the given discrete random variables provided by Probability Mass Function. Here discrete essentially means that there are a set number of outcomes for the variables. For understanding discrete variables better, the set number of outcomes in a die can only be 1, 2, 3, 4, 5 or 6. Here a discrete random value when considering a die is a set of random variables which are finite.

Probability Mass Function properties are unique, and they set it apart from probability density function. PMF is a part of the Probability Distribution Function. A function which is used to denote a probability distribution is a Probability Distribution Function. All the probabilities for the given discrete random variables provided by Probability Mass Function. Consider a discrete random variable X, its probability mass function is assigned by allocating a probability that X is equal to all of its possible values. For characterizing discrete random variables, its probability distribution can be attributed to probability mass function. Probability Mass Function is denoted by P(x). Let us get back to the example of a six-sided die. The probability of rolling a 4 is f(4) = 1/6.

A variable that can possibly take on any number on a continuum is considered as a continuous random variable and is used in PMF. Example of a continuous random variable is a set of all real numbers. Just like probability mass function, we cannot assume that the probability of X is exactly as of each given values. A probability density function and probability mass function is different, so we essentially assign the probability of value X as near to each value in pdf.

Random variables may be any number out of the hat or numbers from the dice and more. A random variable is subject to any changes due to random variations that may take place. You often think of a random variable as an outcome of a random experiment like flipping a coin for heads or tails, rolling a die.

The probability mass function and probability density function for discrete random variables and continuous random variables respectively are similar as we use integrals in the former and sums in the latter. The formula for probability density function is Pr(X∈A)=∫Aρ(x)dx.

The equation for PMF is f(x)= p(X=x). This formula means that the probability that X takes on the value x. Most commonly PMF is plotted on a graph for easy interpretation of the subject under study.

We certainly need probability distribution to understand the likelihood of a scenario so as to be ready for the outcome in advance. Here the probable value will eventually bounce between the maximum and the minimum variables. It depends on the number of factors as to where the plotting of a probability distribution will take place.

For a successful data scientist knowledge about probability mass function becomes quintessential for being competent. Now that you know Statistical skills are utilized to acquire the required data and are processed to arrive at a decision by using different statistical methods like

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]]>The post Inferential Statistics – Definition and Types appeared first on AcadGild.

]]>There are many differences between descriptive and inferential statistics**.** Inferential statistics is extremely useful in data analytics, and any capable data scientist must have an idea of what it is in order to understand and solve many real-world problems fully.

So what is inferential statistics, and what are the types of inferential statistics? Let us find out.

Inferential statistics is generally used when the user needs to make a conclusion about the whole population at hand, and this is done using the various types of tests available. It is a technique which is used to understand trends and draw the required conclusions about a large population by taking and analyzing a sample from it. Descriptive statistics, on the other hand, is only about the smaller sized data set at hand – it usually does not involve large populations. Using variables and the relationships between them from the sample, we will be able to make generalizations and predict other relationships within the whole population, regardless of how large it is.

There are many tests in this field, of which some of the most important are mentioned below.

In this test, a linear algorithm is used to understand the relationship between two variables from the data set. One of those variables is the dependent variable, while there can be one or more independent variables used. In simpler terms, we try to predict the value of the dependent variable based on the available values of the independent variables. This is usually represented by using a scatter plot, although we can also use other types of graphs too.

This is another statistical method which is extremely popular in data science. It is used to test and analyse the differences between two or more means from the data set. The significant differences between the means are obtained, using this test.

This is only a development on the Analysis of Variance method and involves the inclusion of a continuous co-variance in the calculations. A co-variate is an independent variable which is continuous, and are used as regression variables. This method is used extensively in statistical modelling, in order to study the differences present between the average values of dependent variables.

A relatively simple test in inferential statistics, this is used to compare the means of two groups and understand if they are different from each other. The order of difference, or how significant the differences are can be obtained from this.

Another extremely useful test, this is used to understand the extent to which two variables are dependent on each other. The strength of any relationship, if they exist, between the two variables can be obtained from this. You will be able to understand whether the variables have a strong correlation or a weak one. The correlation can also be negative or positive, depending upon the variables. A negative correlation means that the value of one variable decreases while the value of the other increases and positive correlation means that the value both variables decrease or increase simultaneously.

Now that you know what inferential statistics are and how important these tests are, you can start your journey to become a capable data scientists using the many courses that Acadgild has to offer!

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]]>The post Why Learn Python? 6 Reasons to Start Learning Python appeared first on AcadGild.

]]>Python can be used for almost all types of programming – you name it, it can do it. Be it statistical modelling in python, scientific of mathematical computing, finance, trading, game development or even penetration testing, python is suitable for all those activities. In fact, most of the python programmers say that almost all problems in programming can be solved using this multi-functional programming language.

Data science is the most trending aspect of computer programming today, and many companies have already started implementing analytics systems regardless of the field they operate in. Python and statistics have almost become synonymous now, and are usually mentioned in the same sentences as each other. Python is currently the most popular application in data engineering, and this has been one of the main reasons why it has become extremely popular today. Tools like NumPy, SciPy, Pandas and the many others available really do make life easier for a data scientist. Prototyping is remarkably easy using Python, and it would be the best choice for you if you want to build a career as a data scientist.

Make no mistake, this is perhaps the most important and attractive part of why Python has become so popular. Some of the highest salaries in the industry, especially in the US, go to capable Python developers. Python is the second best-paying programming language to know, and it is only beaten by a small margin by Ruby – which is an extremely niche language to know. Considering its flexibility and ease to learn, Python is definitely the better one to start with among the two.

As iterated earlier, the demand for capable Python developers is growing by the day. As companies become increasingly data-oriented, data analysts and scientists are finding increasingly lucrative job opportunities. There has been a steady growth in the demand for Python programmers ever since 2012, and it is within reach of most interested people. Start on your journey to learn statistics using Python if you want to tap into this!

Anyone who has learned Python would testify to the fact that it is an extremely efficient and quick language to use. Making any sort of application using Python would take a lot less time than using other languages, and the coding process is also considerably quicker than most. Also, learning Python from scratch is also extremely easy, since it does not have a rigid syntax like the other programming languages.

By now, you would have understood that Python is extremely beginner-friendly to learn. The code reads almost like English, and there are very few aspects regarding the syntax that you would have to learn to use it. Beginners will be able to focus on solving the process at hand, instead of having to spend a lot of time debugging the code for small syntax mistakes.

If you really want to start learning Python for data science, check out the courses offered by Acadgild!

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