Data Science Tools: Almost every one of us depends on the internet today and thus we share data directly or indirectly. WhatsApp, Facebook, Instagram, Google, Uber, Zomato – all of them and others continuously mine user data to know more about the user and provide them a better web experience each time.
The processing and modeling of data to solve real-world problems are what Data Science is all about.
Who can learn data science?
Whether you are from a math background or not, whether you have worked with SQL or a programming language before or not, if you love to crack puzzles and can find links, patterns, and behaviors – data science is for you.
Data science can be performed with ease using tools. There are various tools for programmers (or those with programming knowledge) and for business analysts (who may not have programming knowledge). For example, Hadoop, Python, R, SQL etc are some tools used by programmers, whereas business users use tools like Rapid Miner, KNIME, PowerBI, etc… Learning at least a few of these data science tools will cover a considerable part of your data science learning journey.
Many tools like R, Python, RapidMiner, Tableau, Power BI help with each stage of data science that can be automated – data preparation, performing analysis, applying algorithms and data visualization. We will discuss step by step, the tools that can be used at each stage.
Apart from the ones mentioned in this article, there are many more tools available in the market, each having its own set of features and benefits, but these are the most popular and learning them will never be a waste of time (ever!).
So, how is data science done?
Data science is vast and seems complex. But, when broken down into sub-stages, you will notice that all you need is thorough technical knowledge and good organizational skills. The main steps involved in data science are –
- Data collection and problem creation
- Data wrangling (data munging or data preparation)
- Data mining and Machine learning
- Data visualization
- Generating potential business solutions
Best Data Science Tools
Each of these steps requires a different set of data Science tools based on the type of business problem and in this article, we will learn about the tools used in each step.
1. Data collection and problem creation tools
Every business evolves through innovation. If you start a simple ‘Dosa’ business and after 2 years, if you are still selling the same Dosa without any improvements, someone else who also started giving ‘Cheese Dosa’ or ‘Maggi Dosa’ will steal your chances of giving better services. Constant up-gradation and innovation are essential for the success of a business.
But, how will you know what to do? – By knowing your customers!
Ask for feedback and suggestions from your customers, know how your competitors are doing, know if your employees are happy and so on. Here are some questions that you can ask –
- Why do people like my product? (Uniqueness)
Maybe the chutney has that extra smooth texture and the subtle touch of home-made food.
- Who are my customers? How can I increase my customer base? (Expansion)
Mostly office goers who stay away from home (let us say the age group of 25-35 years) and need quick bites eat dosas. How about catering to college students and senior citizens?
- Is there a target audience that does not like my product? Why? (Feedback)
Maybe the side dishes like chutney or spice powder is too spicy for kids. How about including kid-friendly dosas, that has loads of cheese, fresh tomato sauce, and less spicy gravies?
- How can I improve my services more to attract more consumers? What are my competitors doing?
Collect more data! The dosa seller on the next street has introduced a ‘customizable dosa’ platter, where customers can choose how they want their Dosa… What can I do?
The essence of the entire problem-solution chain is the collection of relevant data and channelizing it to solve complex business problems.
How is data collected?
Data collected from each consumer is called raw data. If such complex business problems as the above were to be solved, data from many such consumers needs to be collected and processed. That is why it is referred to as Big Data.
Big data collection can be quantitative or qualitative and can be done in many ways –
- Querying the existing internal database
- Purchasing external datasets that contain potentially useful information
- Conducting case studies and interviews
- Surveys, questionnaires, community forums, and feedback forms
- Collecting data through CRM software and transferring it into a file with .csv or. xl format
Data collection is important and should be comprehensive. If not done properly, the analysis could be skewed leading to results that are useless or ineffective for business.
The raw data collected is not always perfect and needs to be made right for further processing. The process of extracting useful details by cleansing and filtering relevant data and enriching it for further exploratory analysis is called as data preparation or data ‘wrangling’. The quality of the final data totally depends on this step.
Data Science Tools to handle big data
To analyze the huge amount of data is not possible unless it is organized and stored properly. What you receive from different sources (data collection) is raw data that has to be transformed and mapped into a more appropriate and usable format – known as data munging (or preparation or wrangling).
Querying the database and collecting relevant information becomes much faster when data is organized well. For example, you can collect data on how much sales your Dosa shop makes on a weekend vs during weekdays, what are the most popular items ordered, what times of the day see more traffic/sales – do people prefer dosas during morning or evening or night, and so on.
The most popular tools to handle big data are –
Using SQL to query and fetch data is the traditional and robust way and always works. SQL has a lot of features to filter, query and fetch the records as required. However, it is costly and needs a lot of setups to be done.
This is the most popular type of NoSQL that offers high performance and has a cloud-based infrastructure. The schemas are dynamic unlike in SQL, making it a great choice for data analysis.
It is a big data tool that follows the structure of a graph database, i.e. storing graphs like demographic patterns, social networking issues, etc… It has high availability, is scalable and flexible. It also supports graphs of the query language (Cypher).
Data Preparation or Data wrangling tools
It is essential to prepare data for analysis. For example, there might be some information missing in the data collected or the existing data may be insufficient to come to a definitive conclusion. Raw data is extracted from the various sources, formatted, parsed, sorted, filtered, cleansed, arranged into appropriate data structures and stored in the database or other storage systems for analysis.
Yup! That’s all!
As datasets are not usually in a perfect state, they can contain a lot of errors, your focus while cleaning and preparing data should be on answering just one question – What am I trying to learn or achieve by cleaning this data?
If that’s in your mind, the task becomes easier and more focussed.
Cleansing and data preparation can be done with the help of following tools –
1. Apache Spark and
Both Spark and Hadoop are open-source and have been developed specifically to handle big data. Both give a great performance. Hadoop is a low cost; however, Spark is faster than MapReduce used by Hadoop. Spark can do real-time processing.
2. Microsoft Excel
Information pre-processing is quite easy with excel spreadsheets. It can be used for cleaning, filtering, slicing and applying formulae on relatively smaller datasets and also provides integration with SQL. Excel is irreplaceable when it comes to analyzing and providing insights on smaller sets of data (not big data).
3. SAS Data Preparation
This is a great tool, especially for non-programmers and provides a friendly interface where you can just click on the required functions to do preparation tasks. The tool helps save a lot of time, thus improving overall productivity. One can visually explore and load data sets from .csv, text, Hadoop and other sources. SAS also lets users define templates that they can use later for another analysis. SAS is costlier and used mostly by business analysts in large organizations.
Currently, the most popular programming language and data preparation or cleaning tool, Python cleans and transforms data quickly and efficiently. Python has an extensive library known as Pandas for data analysis and manipulation. There are many other features of Python that we will discuss further in this article.
5. Power BI
Microsoft PowerBI is a self-service tool that helps you build a single repository by formatting, remapping, schematizing and transforming received data. The final data is consistent and ready to use for further analysis. PowerBI uses dataflows to easily run the data preparation logic cutting down the time and cost required for data preparation.
With Alteryx, data preparation can be done visually. You can prepare data sources, cleanse, format, join, transpose and enrich the datasets from the raw data collected in very less time. This helps users spend more time on analytics than on preparation. Alteryx is costlier than the other options we have discussed so far.
Trifacta’s data wrangling software helps in self-service data preparation and exploration. The connectivity framework of the software connects various sources like Hadoop, cloud services, .cvs, .xml, .txt or other files and relational databases securely and wrangles them to be used across platforms.
Data Transformation tools
Data transformation and data wrangling is not different processes but used in different contexts. While data wrangling is done by business analysts, executives and non-technical professionals, data transformation (Extract-Transform-Load (ETL)) is done by skilled IT professionals who have worked with data for quite some time.
In today’s business and data needs, ETL and wrangling go hand-in-hand. Companies do both so that data transformation is easy from the data source, and business executives can still have the flexibility to quickly check the insights they want. This approach gives better precision for critical projects. Some popular ETL tools are –
1. Microsoft SQL Server Integrated Services (SSIS)
SSIS is a flexible and fast tool for data transformation including cleaning, joining, aggregating data. It can merge data from many data stores and standardize it. The tool offers great performance and can migrate over millions of records in a few seconds.
2. Oracle Data Integrator
With a nice graphical interface, ODI simplifies the data transformation process through its declarative rules-driven approach for a great performance at a low cost.
3. SAS Data Integration studio
This visual studio is a part of the SAS tool for data science. It is a powerful tool that integrates data from multiple sources and builds and visualize the entire data integration process.
4. SAP Data Integrator
With SAP data integrator, data migration is accurate and fast. Scalability is another plus point of this tool. Compared to other systems, SAP integrators can return answers to complex queries quite faster. It provides support for bulk-batch ETL and enhanced support for text processing. Price is a bit higher than other tools though.
Matillion has been specifically developed for transforming cloud data warehouses. It can extract and load data from APIs, applications, SQL and NoSQL databases and files and transform it into data that can be used by ML algorithms for deeper analysis.
6. Informatica PowerCenter
Informatica is database neutral and hence can transform data from any source. It is a complete data integration platform that can perform data cleansing, profiling, transforming and synchronization.
Exploratory Data Analysis (EDA) tools
EDA is a concept of statistics where data sets are analyzed to obtain main characteristics, mostly using visual methods. It is like playing with the data and exploring it before applying any algorithms or models on it. EDA can lead to the formation of certain hypotheses that could be useful to understand if any more data collection is required for deeper data analysis. There are many graphical techniques used for performing EDA, the most popular being scattered plots, histograms, box plots, multidimensional scaling, Pareto chart, etc… and some quantitative techniques like ordination, median polish, etc… Some popular tools that provide a comprehensive way to use these techniques are –
1. R and RStudio
R is one of the earliest and neatest tools that not only clean but also provides rich resources for data analytics. It works with all the operating systems and when combined with programming languages like C++, Java or Python, the capabilities and features are more than doubled. It is the perfect tool that provides extensive libraries for data cleaning, statistical computing, and analytics. You can choose to use the commercial or open-source version. R studio is nothing but an IDE where you can write and run R programs quickly and efficiently.
For the love of coding, you should choose Python. It has extensive libraries that can perform a lot of statistical (descriptive) analysis, correlation analysis, and simple modeling, quickly and easily. It is easy to find missing values, outliers, anomalies and unexpected results in the data and then look at overall patterns and trends in a visual manner (like boxplot, scatter plots, histograms, heatmaps) using Python.
SAS has powerful data analysis abilities and uses the SAS programming language as the base. The tool has been particularly designed for statistical analysis and can support many data formats. PROC UNIVARIATE in SAS provides various statistics and plots for numeric data.
This is an open-source EDA tool based on Eclipse. KNIME is great for those who do not want to do much programming to achieve the results they want. It can perform data cleansing and pre-processing, ETL, data analysis and visualization. KNIME has been widely used in pharmaceutical and financial domains, and for business intelligence.
5. Microsoft Excel
For a regular smaller chunk of data, Excel provides some great insights and you can look at data from various angles to make quick plots and pivot tables, that are really helpful for quick analysis and moving on to the next step. Excel doesn’t need you to have any programming experience and has been there in your system to do other office operations, so you don’t have to spend any extra time, money or resources.
Machine learning and decision-making tools
With good EDA, you will be able to get qualitative analysis. With algorithms and machine learning tools, you can quantify the analysis and build a predictive model for your business. For example, with EDA, you know that the sales of paneer dosa were way less than that of masala dosa in the last quarter. But now we have to know ‘why’ through diagnostic analysis and then build a model that will determine how the sales will be in the next quarter (read more about different types of analysis here). There are several tools that help in employing ML techniques like linear regression, decision trees, naïve Bayes classifier algorithms and others to understand why something went wrong and then predict the future based on both the past and present data.
1. R & Python
When it comes to programming, R and Python are ruling the list of machine learning tools. Python’s easy syntax and a host of libraries and tools are suited for ML. Some Python tools designed for ML are Pattern, Scikit-learn, Shogun, Theano, Keras, etc… In the same way, R has a host of libraries and packages that make ML easier – e1071, rpart, igraph, kernlab, randomForest, trees and many more. Check out the most popular ones here.
Weka is a collection of machine learning algorithms that you can apply directly to a dataset or call from your code. It contains tools for data mining tasks, including data preprocessing, regression, classification, clustering, association rules. It has a neat GUI that will help beginners to understand machine learning well and can be used in combination with R (rweka).
One of the most popular tools that support all the data science steps starting from data preparation, machine learning, deep learning, text analytics, model validation, data visualization, and predictive analytics. Some older versions of RapidMiner are open-source. RapidMiner is available for both commercial and educational purposes like research, training, and education. You can design models using visual workflow designer or automated modeling, deploy, test and optimize the model for insights and actions.
It is an open-source, distributed ML tool that is scalable, flexible and widely used for linear models and deep learning algorithms. H2o’s compute engine provides end-to-end data science services from data preparation, EDA, supervised and unsupervised learning, model evaluation, predictive analysis and model storage. H2o offers complete enterprise support to boost the deployment process making AI easier and faster.
MATLAB is a simple yet powerful tool that can be learned easily. It is used for plotting of data and functions, manipulations of matrices, implementation of algorithms and creation of user interfaces. If you were an engineering student, you may have written some MATLAB programs during college days. If you have prior knowledge of C, C++ or Java, learning and implementing ML algorithms will be a breeze for you.
Another powerful automated machine learning tool where you can prepare your dataset, drag and drop elements to apply models and algorithms, build and retrain the applied model and make accurate predictions and insights. DataRobot is being extensively used in domains like healthcare, finance, banking, public sector marketing amongst others. It can also use libraries from R, Python, and H2o for even faster and more accurate results.
TensorFlow is a free and open-source library designed by Google Brain team originally for their internal use. Tensor Processing Unit, built by Google works beautifully for TensorFlow and is designed specifically for machine learning. With both these combined, you can develop and train complex ML models, iterate and retrain the models and quickly make them ready for production. Other than Google, Airbnb, Coca-Cola, Intel and Twitter are some big names using TensorFlow.
Data Visualization tools
Okay, so we applied our analysis, created a model, trained it – what’s next?
We need to present the information that we have gathered in some form that is easily understandable and efficient. This is where Data Visualization comes into the picture. The insights, patterns and other detected relationships among data are graphically presented so that business analysts and other stakeholders can make informed business decisions.
Complex information is retained in our memories for a longer time when it is in the form of pictures and visuals rather than text. Data visualization engineers need to be adept in mathematics, graphics, statistical algorithms, visual tools and correct usage of the tools for different business scenarios.
There are many tools of which programmers and researchers use R, Python, and ggplot2. For small sets of data, like for daily sales reports or employee leave information, tools like Excel are good enough. or big data and commercial products with complex data, some popular tools are Tableau, PowerBI, D3, FineReport, etc…
With Tableau, users can just drag and drop elements to create visualizations. Visualizations are created in the form of dashboards and worksheets. While it is majorly used for visualization, Tableau can also perform data preparation. Tableau is faster than excel and can integrate with different data sources to directly create charts, reports, and dashboards. There are no programming skills required to be able to work on Tableau.
Qlik is a BI tool just like Weka, RapidMiner, etc… that can perform end-to-end data science process i.e. data integration, data analysis, and data visualization. QlikView is a powerful tool for data visualization that gives insights in the form of dashboards. With QlikView reporting efficiency is increased by more than 50% and teams can focus on more important business tasks. It is easy to learn and implement and an ideal tool for business intelligence.
ggplot2 is a data visualization package created for R. It is an enhancement over the basic graphics provided in R and data can be transformed many layers or scales to provide powerful charts. A similar plotting system is available for Python and is known as ggplot. Both ggplot and ggplot2 require minimal code to generate professional-looking graphs in less time.
FineReport – You can create beautiful visualizations using this tool that has over 19 categories and 50 styles to represent data in various ways. The dynamic 3D effects are interactive and help business users understand the insights quickly and accurately. FineReport gives a rich display of graphical information and geographic information making it a great choice for complex business problems.
Are you overwhelmed with the above list of tools? Worry not! Let me tell you a secret –
If you are a programmer, start with R or Python and then move on to other automated products that make your life simple. If you are not a technical person, start with Microsoft Excel, Tableau and Weka. Remember that these tools are to help you, not confuse you. Many of these have a free version, so before choosing one, you can play around with some and then decide which one you find most suitable for your specific problem.
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