Robotics Engineer | Data Analyst.

Data Analytics has been highly effective in tackling most of the real-world scenarios like enhancing customer experience, cost-reduction, targeted marketing, and it also helps in creating content strategies and developing products based on customer/client feedbacks.

**“Data** is a precious thing and will last longer than the systems themselves.” — Tim Berners-Lee, inventor of the World Wide Web.

Data is at present considered to be the catalyst for any organization’s growth. As a result, more and more companies are leaning towards data science for their own growth and development. …

A time series is a sequence or series of numerical data points fixed at certain chronological time order. In most cases, a time series is a sequence taken at fixed interval points in time. This allows us to accurately predict or forecast the necessities.

Time series uses line charts to show us seasonal patterns, trends, and relation to external factors. It uses time series values for forecasting and this is called extrapolation.

Time series are used in most of the real-life cases such as weather reports, earthquake prediction, astronomy, mathematical finance, and largely in any field of applied science and engineering. …

As we all know, data analytics is the process of collecting, engineering, analyzing of data's from an organization to gain insightful information which can help in the growth and development of the organization. Since the introduction of Data Analytics, various new terms and trends were introduced to us such as machine learning, artificial intelligence, Iot etc. which works hand in hand with data science and this field is flourishing more greater than before. …

When we work on a huge dataset, we usually see a large number of variables scattered with huge amounts of variances among them which makes it difficult to work with and in turn reduces the efficiency of our model. When working with such kind of large datasets, it’s nearly impossible and exhausting to individually engineer every variable. That’s when principal component analysis comes into play.

Multivariate analysis often starts with a huge number of correlated variables. …

With the advancement of technology, companies have been able to produce and analyze more and more data every day. Large amounts of data are spewed out every second. Data analysis has spread into so many different fields and so the world has been introduced to many advanced concepts like machine learning and artificial intelligence.

In order to build a brilliant ML or AI model, we need clean data, and we need tons of it. The more the data, the better the output, and the more cleaner the data, better the efficiency of our model. The larger the size of the data, the more difficult is its storage and management and that’s where CLOUD comes in. …

*ANOVA *stands for analysis of variance. It is a collection of statistical models and their associated estimation processes used to analyze differences among group means in a sample.

People usually get confused regarding the working of ANOVA as it says “Analysis of variance” but in simple terms, ANOVA is used to compare the differences in means in more than 2 groups and it does this by looking at the variation in data and where the variation is found.

It helps us identify which of the experiment or surveys are more significant or in other words it tells us whether to reject the null hypothesis or accept the alternate hypothesis. …

SQL stands for **S**tructured **Q**uery **L**anguage and is mainly used for management of data in database. Almost every company on this planet stores data in the form of rows and columns or *tables* and these tables are stored in what is called a **r**elational **d**atabase **m**anagement **s**ystem (**RDBMS**).

SQL is used to create, insert, delete, update, organize, or in other words manage all the data's in the database. …

With the evolution of technology and growth of data, The term Analytics has derived several meanings over the years. Depending on the type of data, size of data, method of working with data, type of queries that needs to be answered, Analytics has been grown into several types.

The magic about this field is that the resources available are unlimited. Be it the data that we need to work with or the tools we need to use, We have TONS of ways of going about it. It’s a blessing and a curse. It’s a blessing because we have not one, but MANY ways of working with data. The magic is, THAT’s the curse too…because we then have one too many ways of getting confused. …

GoogleVis is a package which acts as a link or an interactive platform between R and Google Charts API.

In R we can find a lot of visualization methods such as the histogram, bar plot, scatter plot etc.

GoogleVis provides us with more better, advanced charts such as the most common, motion chart, which can be utilized without even uploading our data's into the google server.

The output of googleVis is an HTML code that contains the data and references to JavaScript functions hosted by Google. GoogleVis makes use of the R HTTP server to display the output locally. …

What is Exploratory Data Analysis?

Exploratory Data Analysis is nothing but just like the name suggests, is the method of studying and analyzing the data thoroughly before the final decision-making process. In exploratory data analysis, the data is analyzed mostly visually to summarize various characteristics of the given data set.

In simple terms, It’s just like choosing which movie to go to. Before going in for a movie, we tend to check the ratings and reviews and basic storylines, etc.. doing the same thing for a particular data set is what data analysts call as Exploratory Data Analysis.

So is Exploratory Data Analysis same as Data Analysis?…