Living in the 21st century, you might have often come across the word ‘data analytics’. Currently, it is one of the most buzzing terminologies. For those who want to begin their journey in data analytics, then this is the right read for you.
This blog is your quintessential guide to what is data analytics and will help you understand the subject from scratch. For all you beginners who like playing with data, this is your learning curve for an enriching career. Empower your journey with Simplilearn's Data Analytics Bootcamp. Gain hands-on skills, solve real industry challenges, and excel in the world of Data Analytics.
What is Data Analytics?
Data analytics takes raw data and turns it into useful information. It uses various tools and methods to discover patterns and solve problems with data. Data analytics helps businesses make better decisions and grow.
Companies around the globe generate vast volumes of data daily, in the form of log files, web servers, transactional data, and various customer-related data. In addition to this, social media websites also generate enormous amounts of data.
Companies ideally need to use all of their generated data to derive value out of it and make impactful business decisions. Data analytics is used to drive this purpose.
Ways to Use Data Analytics
Now that you have looked at what data analytics is, let’s understand how we can use data analytics.
1. Improved Decision Making: Data Analytics eliminates guesswork and manual tasks. Be it choosing the right content, planning marketing campaigns, or developing products. Organizations can use the insights they gain from data analytics to make informed decisions. Thus, leading to better outcomes and customer satisfaction.
2. Better Customer Service: Data analytics allows you to tailor customer service according to their needs. It also provides personalization and builds stronger relationships with customers. Analyzed data can reveal information about customers’ interests, concerns, and more. It helps you give better recommendations for products and services.
3. Efficient Operations: With the help of data analytics, you can streamline your processes, save money, and boost production. With an improved understanding of what your audience wants, you spend lesser time creating ads and content that aren’t in line with your audience’s interests.
4. Effective Marketing: Data analytics gives you valuable insights into how your campaigns are performing. This helps in fine-tuning them for optimal outcomes. Additionally, you can also find potential customers who are most likely to interact with a campaign and convert into leads.
Let’s now dive into the various steps involved in data analytics.
Steps Involved in Data Analytics
Next step to understanding what data analytics is to learn how data is analyzed in organizations. There are a few steps that are involved in the data analytics lifecycle. Let’s have a look at it with the help of an analogy.
Imagine you are running an e-commerce business and your company has nearly a million in customer base. Your aim is to figure out certain problems related to your business, and subsequently come up with data-driven solutions to grow your business.
Below are the steps that you can take to solve your problems.
Fig: Data Analytics process steps
1. Understand the problem: Understanding the business problems, defining the organizational goals, and planning a lucrative solution is the first step in the analytics process. E-commerce companies often encounter issues such as predicting the return of items, giving relevant product recommendations, cancellation of orders, identifying frauds, optimizing vehicle routing, etc.
2. Data Collection: Next, you need to collect transactional business data and customer-related information from the past few years to address the problems your business is facing. The data can have information about the total units that were sold for a product, the sales, and profit that were made, and also when was the order placed. Past data plays a crucial role in shaping the future of a business.
3. Data Cleaning: Now, all the data you collect will often be disorderly, messy, and contain unwanted missing values. Such data is not suitable or relevant for performing data analysis. Hence, you need to clean the data to remove unwanted, redundant, and missing values to make it ready for analysis.
4. Data Exploration and Analysis: After you gather the right data, the next vital step is to execute exploratory data analysis. You can use data visualization and business intelligence tools, data mining techniques, and predictive modeling to analyze, visualize, and predict future outcomes from this data. Applying these methods can tell you the impact and relationship of a certain feature as compared to other variables.
Below are the results you can get from the analysis:
- You can identify when a customer purchases the next product.
- You can understand how long it took to deliver the product.
- You get a better insight into the kind of items a customer looks for, product returns, etc.
- You will be able to predict the sales and profit for the next quarter.
- You can minimize order cancellation by dispatching only relevant products.
- You’ll be able to figure out the shortest route to deliver the product, etc.
5. Interpret the results: The final step is to interpret the results and validate if the outcomes meet your expectations. You can find out hidden patterns and future trends. This will help you gain insights that will support you with appropriate data-driven decision making.
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Types of Data Analytics
There are four types of data analytics:
- Predictive analytics
- Descriptive analytics
- Prescriptive analytics
- Diagnostic analytics
Data Analytics Tools
Now that we looked at the different steps involved in data analytics, let’s see the tools involved in data analytics, to perform the above steps. In this blog, we will discuss 7 data analytics tools, including a couple of programming languages that can help you perform analytics better.
1. Python: Python is an object-oriented open-source programming language. It supports a range of libraries for data manipulation, data visualization, and data modeling.
2. R: R is an open-source programming language majorly used for numerical and statistical analysis. It provides a range of libraries for data analysis and visualization.
3. Tableau: It is a simplified data visualization and analytics tool. This helps you create a variety of visualizations to present the data interactively, build reports, and dashboards to showcase insights and trends.
4. Power BI: Power BI is a business intelligence tool that has an easy ‘drag and drop functionality. It supports multiple data sources with features that visually appeal to data. Power BI supports features that help you ask questions to your data and get immediate insights.
5. QlikView: QlikView offers interactive analytics with in-memory storage technology to analyze vast volumes of data and use data discoveries to support decision making. It provides social data discovery and interactive guided analytics. It can manipulate colossal data sets instantly with accuracy.
6. Apache Spark: Apache Spark is an open-source data analytics engine that processes data in real-time and carries out sophisticated analytics using SQL queries and machine learning algorithms.
7. SAS: SAS is a statistical analysis software that can help you perform analytics, visualize data, write SQL queries, perform statistical analysis, and build machine learning models to make future predictions.
Now that you have seen the data analytics tools, let’s jump ahead and see the applications of data analytics.
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Data Analytics Applications
Data analytics is used in almost every sector of business, let’s discuss a few of them:
1. Retail: Data analytics helps retailers understand their customer needs and buying habits to predict trends, recommend new products, and boost their business.
They optimize the supply chain, and retail operations at every step of the customer journey.
2. Healthcare: Healthcare industries analyze patient data to provide lifesaving diagnoses and treatment options. Data analytics help in discovering new drug development methods as well.
3. Manufacturing: Using data analytics, manufacturing sectors can discover new cost-saving opportunities. They can solve complex supply chain issues, labor constraints, and equipment breakdowns.
4. Banking sector: Banking and financial institutions use analytics to find out probable loan defaulters and customer churn out rate. It also helps in detecting fraudulent transactions immediately.
5. Logistics: Logistics companies use data analytics to develop new business models and optimize routes. This, in turn, ensures that the delivery reaches on time in a cost-efficient manner.
Those were a few of the applications involving data analytics. To make things simpler, this blog will also focus on a case study from Walmart. Here you can observe how data analytics is applied to grow a business and serve its customers better.
Walmart Case Study
The American multinational retail company- Walmart has over 11,500 stores in 27 countries worldwide. It also has e-commerce websites in 10 different countries. Walmart boasts more than 5,900 retail units. These units operate outside the United States, with 55 banners in 26 countries. It has more than 700,000 associates serving more than 100 million customers every week. In short, it’s a pretty huge company.
With all these big numbers, you can imagine the exponential amount of data Walmart generates. Walmart collects over 2.5 petabytes of data from 1 million customers every hour. Yes, you read that right. Now to make sense of all this information, Walmart has created ‘Data Café’ – a state-of-the-art analytics hub.
In Data Cafe, over 200 streams of internal and external data, including 40 petabytes of recent transactional data, can be modeled, manipulated, and visualized.
Walmart also constantly analyses over 100 million keywords to know what people near each store are saying on social media. This gives them a better understanding of their customer behavior on what they like and dislike.
This global chain uses modern tools and technologies to derive business insights and improve customer satisfaction. Some of these technologies include Python, SAS, and NoSQL databases such as Cassandra and Hadoop.
Using all these technologies and data analysis techniques, Walmart can better manage its supply chain, optimize product assortment, personalize the shopping experience, and give relevant product recommendations.
Data analytics for beginners should not merely be theoretical, but also be practical. Data analytics is a lot more practical than theoretical. Hence, here we will have a look at a demo on data analytics for beginners exclusively.
Demo on Data Analytics for Beginners
Companies perform data analytics to predict sales and profit. In this demo, we’ll predict sales based on the advertising expenditure using the Linear Regression model in R. The advertising expenditure has been made via different mediums such as Television, Radio, and Newspaper.
Below is the data set for our demo:
We will be using the R programming language for this purpose.
- R is open-source software that can be downloaded from the R Cran website.
- It is easy to learn and implement.
- The R language is built specifically for performing statistical analysis, data manipulation, and data mining using packages like plyr, dplyr, tidyr, and lubridate.
- R supports data visualization with the help of packages such as ggplot2, googleVis, R color brewer, leaflet, and ggmap.
- The R software can also be used in a wide range of analytical modeling including classical statistical tests, linear/non-linear modeling, data clustering, time-series analysis, and more.
So, let’s get coding!
- First, let us install all the necessary packages that we need for this demo.
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install.packages("dplyr") library(dplyr) install.packages("broom") library(broom) install.packages("caTools") library(caTools) # Install the caTools package which will help us build our linear regression model install.packages("ggplot2") # Install the ggplot2 package which we’ll use for data visualization library(ggplot2) |
- The next step is to load the data set.
- For this, you can use the read.csv function and provide the path location where your data is located, followed by the dataset name and the extension. You can assign the loaded dataset to a variable.
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ads<-read.csv("C:/Users/provide the file path/Advertising.csv") |
- Now, let us move ahead and perform the following operations:
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head(ads) # Looks at how our dataset is dim(ads) # Gives the total rows and columns present in the dataset summary(ads) # To get a summary of the dataset |
- Next up, let’s do some data visualization to visualize our data. Since our data has only numeric values, using scatter plots would be the best option. So, let’s visualize our sales against each of the independent variables. For that, we’ll use the plot function and give sales on the x-axis and the independent variable name on the y-axis.
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plot(ads$sales,ads$TV, type = 'p', col="red") # Gives a look at how our dataset is |
- The red dots are pretty much aligned in one direction. This means that, if we are increasing the expenditure on TV ads, the units sold are increasing concurrently. So, the more you spend on TV ads, the more sales you can expect.
- Next, let’s look at how sales vary based on radio advertising expenditure.
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plot(ads$sales,ads$radio, type = 'p', col="blue") |
- If you look at the blue dots, it is not as linear as compared to our previous graph. There are a few data points that show that the sales were not good, despite spending a good amount of money on radio ads. You can still expect a fair amount of sales if you are willing to spend on radio advertising.
- Now let’s look at how sales vary based on newspaper advertising expenditure.
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plot(ads$sales,ads$newspaper, type = 'p', col="green") |
- Note that the plots are very haphazardly present. The data is entirely non-linear, and there seems to be a low correlation between sales and newspaper advertising expenditure.
- If you want to look at these plots all at a time, you can use the pairs function.
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pairs(ads) |
- Next, let’s check the correlation between the variables and see what insight we can get. We’ll now use the cor function and build a correlation matrix.
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install.packages("corrplot") # To install the corrplot package library(corrplot) num.cols <- sapply(ads, is.numeric) # To grab only the numeric columns num.cols cor.data <- cor(ads[,num.cols]) # To display the correlations between the variables. cor.data |
- You can see that the correlation values are all above zero, which means there is a positive correlation between the variables, and a change in one of the independent variables will have a positive impact on the sales numbers.
- TV ads have a maximum correlation with sales, and the value is around 0.78. Then, there is radio advertising, which correlates to about 0.57 with sales, and finally, newspaper ads have the lowest correlation compared to the other two.
- Next up, you can build a correlation matrix using the correlation plot method.
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corrplot(cor.data,method='color') |
- This is our plot. On the right, you can see the scale -1 for negative correlation, then there’s light red, 0 is almost white, then light blue, and finally dark blue for the maximum positive correlation. The diagonals are dark blue, which represents the same variables in a row and the column. TV ads and radio ads have the next highest correlation, while newspaper ads have the lowest correlation with sales.
- With that, let us look into the most crucial part of this analysis, which is building our regression model. Now, we will examine a simple linear regression model where we’ll take one input variable which is TV ads. We will be using the ‘lm’ function, which stands for the linear model.
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model_simple <- lm(sales ~ TV,data=ads) # To install the corrplot package summary(model_simple) # Check’s the summary
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- So, our intercept estimate is 7.03. The same summary can also be checked using the tidy function present in the broom package.
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tidy(model_simple) # Gives us a tidy presentation of the summary figures model_multiple <- lm(sales ~ TV + newspaper + radio,data=ads)# To build a regression model with more than one input variable summary(model_multiple) |
- Next, let’s look into another example of how you can train a linear regression model using the caTools library. Here, we will set a random seed value of 101.
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set.seed(101) # Now we have to split the data into training and testing sets. We will take 70% for training the data and 30% for testing the model sample <- sample.split(ads$TV, SplitRatio = 0.70) train = subset(ads, sample == TRUE) test = subset(ads, sample == FALSE) model <- lm(sales ~ .,train) # To create model summary(model) # To check the residual collected from the trained model using the residuals function res <- residuals(model) res <- as.data.frame(res) head(res) # To make our predictions using the test dataset sales.predictions <- predict(model,test) sales.predictions |
- Subsequently, let’s combine these predicted sales values to our original sales for the test data. We will use the cbind function and pass it in the columns.
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results <- cbind(sales.predictions,test$sales) results colnames(results) <- c('pred','real') #Assigns the column names using the colnames function and convert it into a dataframe results <- as.data.frame(results) results |
- Meanwhile, let’s find the accuracy of our model by calculating the r squared error value.
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rsq = summary(model_multiple)$r.sq rsq |
- We have successfully built our model and predicted the sales values using Linear Regression in R. Our model can predict 89% of the data correctly.
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Conclusion
This brings us to the conclusion of Data Analytics for Beginners. We learned what data analytics is, the need for data analytics, and the different steps involved in it.
Then, we looked at the various tools used in data analytics and the application of data analytics. Finally, we saw a case study on Walmart and performed a demonstration on Linear Regression in R to predict sales based on advertising expenditure through various mediums.
Do you have any queries? Please feel free to put it in the comments section of this article.
FAQs
1. Why is data analytics important?
Data analytics is important to understand trends and patterns from the massive amounts of data that are being collected. It helps optimize business performance, forecast future results, understand audiences, and reduce costs.
2. What are the 4 types of data analytics?
The 4 types of data analytics are Predictive data analytics, Prescriptive data analytics, Diagnostic data analytics, and Descriptive data analytics.
3. Who is using data analytics?
While data analytics is used by every business to understand their operations, the four top sectors that are using data analytics are Retail, Agriculture, Banking, and Government.
4. What's the difference between data analytics and data science?
Data science is an umbrella term for a group of fields that are used to mine large datasets and focus on deriving meaningful correlations between large datasets. Data Analytics is more focused on uncovering specific trends and realizing actionable insights.
5. What are the different types of data analysis?
The different types of data analysis are Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, Prescriptive Analytics, and Cognitive Analytics.
6. What are the analytical tools used in data analytics?
Many data analytics tools exist, but the top 10 are SAS Business Analytics (SAS BA), QlikView, Board, Splunk, Sisense, Microstrategy, KNIME, Dundas BI, TIBCO Spotfire, and Tableau Big Data Analytics.
7. What is the career growth in data analytics?
Starting as a Data Analyst, you can then progress to Analyst II, Senior Analyst, and then Data Scientist or even Chief Technology Officer.
8. What are data analytics used for?
Data Analytics is used to make sense of large amounts of data to derive insights and trends to improve business growth.
9. Is data analytics a good career?
Yes, data analytics can be a rewarding career with good job prospects and opportunities for growth.
