Lesson 1 of 6By Simplilearn
Last updated on Apr 5, 202132877Living 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. Following are the topics that we will be looking into:
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.
Fig: Data Analytics
Data analytics is the process of exploring and analyzing large datasets to find hidden patterns, unseen trends, discover correlations, and derive valuable insights to make business predictions. It improves the speed and efficiency of your business.
Businesses use many modern tools and technologies to perform data analytics. This is data analytics for beginners, in a nutshell.
Now that you have looked at what data analytics is, let’s understand how we can use data analytics.
Fig: Ways to 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.
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:
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.
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.
Fig: Data Analytics for Beginners - Tools used
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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Fig: Various applications of data analytics
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.
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.
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.
So, let’s get coding!
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) |
ads<-read.csv("C:/Users/provide the file path/Advertising.csv") |
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 |
plot(ads$sales,ads$TV, type = 'p', col="red") # Gives a look at how our dataset is |
plot(ads$sales,ads$radio, type = 'p', col="blue") |
plot(ads$sales,ads$newspaper, type = 'p', col="green") |
pairs(ads) |
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 |
corrplot(cor.data,method='color') |
model_simple <- lm(sales ~ TV,data=ads) # To install the corrplot package summary(model_simple) # Check’s the summary
|
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) |
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$TV, SplitRatio = 0.70) train = subset(ads, sample == TRUE) test = subset(ads, sample == FALSE) model <- lm(sales ~ .,train) # To create model summary(model) # To check the residual collected from the trained model using the residuals function res <- residuals(model) res <- as.data.frame(res) head(res) # To make our predictions using the test dataset sales.predictions <- predict(model,test) sales.predictions |
results <- cbind(sales.predictions,test$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 |
rsq = summary(model_multiple)$r.sq rsq |
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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.
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