This includes tasks such as removing duplicates, handling missing data, correcting errors, formatting data and merging data from different sources.

Data wrangling makes sure that the data is accurate, consistent, and ready for analysis. Without proper data wrangling, data analysis can be unreliable and misleading, leading to incorrect conclusions and decisions. In this article, we will look at the most common data handling methods used in various stages of data wrangling.

Stage 1: Data Cleaning

This first step in data wrangling entails locating and addressing issues with the data’s quality, such as outliers, missing values, and inconsistencies. Cleaning data can be accomplished in a number of ways, including:

Removing missing values: Missing values can skew analysis results. To address this problem, missing values are either removed or replaced with a value that reflects the nature of the remainder of the data points.

Handling outliers: Extreme values that are significantly outside of a dataset’s typical range are known as outliers. By skewing the statistical measures used, outliers can affect the analysis results. To deal with outliers, you can either get rid of them or make them less extreme.

Resolving inconsistencies: Typos, different data formats, or errors in data collection can all lead to data inconsistencies. They can be fixed by using data validation rules to find and fix errors and standardizing the format of the data.

Stage 2: Data Transformation

Data transformation entails changing the data’s original format to improve the data analysis. Data transformation can be accomplished in a number of ways, including:

Normalization of data: The process of normalizing data entails scaling the data so that it falls within a predetermined range. Data normalization is used when variables forming the data have different units of measurement.

Aggregation of data: Combining data from multiple sources or summarizing data at a higher level of granularity are examples of data aggregation. As a result of data aggregation, data may become simpler to analyze.

Encoding data: The process of converting categorical data into a numerical format that can be used in the analysis is known as data encoding. This method is frequently used when the data contains non-numeric values like gender or product category.

Stage 3: Data Preparation

Data preparation is the final stage of data wrangling. Preparing the data for analysis entails selecting appropriate variables, inventing new variables, and formatting the data. Data preparation can be done in a number of different ways, including:

Variable selection: Variable selection entails removing irrelevant variables and locating the most important variables for analysis. Variable selection may improve the accuracy of the analysis and simplify the data to create a more parsimonious model.

Engineering features: New variables are created using the dataset’s existing variables in feature engineering. New features may bring out hidden patterns and improve the accuracy of the analysis.

Conclusion

Because it ensures that the data are in a format that is suitable for analysis, data wrangling is an essential step in the data science pipeline. There are a number of methods that can be used at each stage of the process, which include cleaning, transforming, and feature selection. Data wrangling improves the data quality prior to analysis and helps data scientists derive more accurate insights.

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1. Data Preparation

Typically, data scientists spend almost 80% of their time in data clearing (or cleaning) to improve the quality of data by making it consistent and accurate before using it for analysis. But this task is extremely mundane and time-consuming. When you’re involved in data clearing, you’ll need to handle terabytes of data, across numerous sources, formats, platforms, and functions, on a daily basis while keeping an activity log to avoid duplication. Thus, manual data clearing is a Herculean task but you can’t ignore it because doing so could give rise to inaccurate data, which would make the output unreliable. This could trigger major negative consequences in case the analysis is used for decision-making.

A solution to overcome this problem could be automating manual data clearing and preparation tasks with the use of AI-enabled data science technologies, such as Auto ML and Augmented Analytics.

2. Feature selection

In the modern world, we’re surrounded by and sitting on massive piles of large-scale data that’s high-dimensional. As high dimensionality has its own curse, it’s important to lessen the dimensionality of such data. This is where feature selection can help by reducing the number of predictor variables, thus letting you build a more comprehensive and simpler model. A model with fewer variables is not just simpler to understand and train but is even easier to run while being less prone to be leaky. Thus, such a model can help in preparing clean, comprehensible data and improving learning performance.

When you handle a small number of variables, you won’t have a lot of problems with a predominantly hand-driven process. But when you have limited time on your hands and several variables to deal with, manual feature selection could be a pain. When handling big data, data velocity and data variety too could be big problems in feature selection.

Such problems can be solved with semi-automated or automated feature selection. For instance, to speed things up, you can use scikit-feature, which is an open-source feature selection repository that contains most of the popular feature selection algorithms that are used in recent times.

3. Outliers

Outliers are data points that are at an abnormal distance away from other data points. To put it differently, they’re values that are different from other values in a random sample. In data analysis, outliers can be problematic as they can distort real results or make tests to overlook significant findings. In data distribution with extreme outliers, the distribution can get skewed in the direction of the outliers. This would make it hard to analyze the data. But outliers aren’t necessarily a bad thing always.

If data values are obviously incorrect or impossible, you should definitely remove them. But sometimes when the data doesn’t fit your model, it could be your model that needs to be changed, not the data.

Sometimes, you can come across data sets for which finding appropriate models could be difficult. But this won’t validate discarding the data just because you can’t find a familiar model to fit it. The solution could be simplifying the analysis to use a nonparametric test.

Closing thoughts

If you want to learn more about these problems and how to solve them, enrolling in a data analysis course offered by a leading data science training institute would be a good idea.

The post The Most Likely Problems In Data Analysis? first appeared on Magnimind Academy.

1- What is big data analytics?

Put simply, big data analytics refers to the process of extracting valuable information by analyzing different kinds of big datasets. It’s used to uncover hidden patterns, consumer preferences, market trends etc in order to help organizations in decision making.

There’s a massive amount of data available today and there’s an urgent need to capture, analyze and preserve that data for getting actionable insights out of it. By looking at the data available to a business, it can determine different ways to make good strides to attain positive results.

Today, every company, from small businesses to giant multinationals, has become dependent on data. Now, just think for a moment, what if you could be the person businesses turn to before making any business decisions? This is exactly the place that future big data analytics will hold for you.

2- Why should you learn big data analytics?

If you’re still not convinced enough by the above example, here’re the reasons you should try to become a part of the future big data analytics.

2.1- HUGE JOB OPPORTUNITIES

As organizations begin to realize they cannot make use of big data in terms of capturing, interpreting and using that data, they’ve started to look for professionals who’re capable of doing so. Just have a look at any major job portal and you’ll find that there’re lots of job postings by companies looking for data analysts. This number will eventually continue to increase as data will become more abundant and the number of professionals with skillsets needed for the job will remain low. So, now is the time to get prepared to become a part of future big data analytics.

2.2- DATA ANALYTICS IS AND WILL BE A PRIORITY FOR TOP COMPANIES

To remain competitive in the business landscape, top companies are looking to implement data analytics to explore new market opportunities for their products and services. Today, a huge percentage of major companies consider data analytics as a crucial component of their business performance and a key approach to rise above the competition and this will become even more important with competition increasing over time. It means today’s aspiring big data professionals will be able to become an inherent part of future big data analytics.

2.3- GREAT SALARY ASPECTS

Across the globe, the demand for big data analytics skill is steadily going up with a massive deficit on the supply side. Despite big data analytics considered as a hot job, there’s a large number of unfilled jobs because of the acute paucity of required skills. The difference between demand and supply is only expected to increase. As a result, wages for professionals with data analytics skills are boosting and companies are ready to offer fattier pay packets for the right people. In some countries, data analytics professionals are getting substantially higher compared to their peers in other IT-based professions. This monetary benefit can surely be considered as a great reason to become a big data analytics professional.

2.4- BIG DATA ANALYTICS IS INCREASINGLY GETTING ADOPTED BY ORGANIZATIONS

New technologies in the field are making it easier to perform sophisticated data analytics tasks on diverse and massive datasets. A lot of professionals are using advanced data analytics techniques and tools to perform tasks like data mining, predictive analytics, among others. With big data analytics offering businesses an edge over the competition, companies are implementing a diverse range of analytics tools increasingly. Today, it’s almost impossible to find a top brand that doesn’t take help of at least some form of data analytics. In light of the increasing adoption rate of data analytics, it can be said that the landscape of future big data analytics will hold a good place for skilled professionals.

2.5- YOU’LL BE A PART OF THE CORE DECISION MAKING

For the majority of the companies, big data analytics is a major competitive resource. There’s no doubt that analytics will become even more important in the near future as competition will keep on increasing. This is mainly because there’s a massive amount of data which is not being used and only rudimentary analytics is getting done. It’s an undeniable fact that data analytics is and will be playing a crucial role in decision making, regardless of the volume of an organization. Not being able to be a part of the decision-making process is something that generates dissatisfaction for a significant number of employees. As a big data analytics professional, you’ll be a crucial part of business decisions and strategies, catering to a major purpose within the company.

2.6- YOU’LL HAVE A DIVERSE RANGE OF JOB TITLES TO TAKE YOUR PICK FROM

As a data analytics professional, you’ll have a wide range of job titles as well as domains from which you can choose according to your preference. Since data analytics is used in different fields, lots of job titles like big data engineer, big data analytics architect, big data analyst, big data solution architect, analytics associate, big data analytics business consultant, metrics and analytics specialist etc will be available to you. Also, an array of top organizations like Microsoft, IBM, Oracle, ITrend, Opera are utilizing big data analytics and thus huge job opportunities with them are possible.

2.7- YOU’LL BE ABLE TO BECOME A FREELANCE CONSULTANT

A vast majority of today’s workforce keeps on looking for ways to diversify their income sources and ways through which they can maintain a perfect work-life balance. Data analytics professionals being able to offer valuable insights about major areas hold the perfect position to become a consultant or freelancer for some of the top companies. So, you don’t need to be tied to a single company. Instead, you’ll be able to work with multiple organizations who’ll depend on your insights when making crucial business decisions.

3- Key skills you should focus on to become a part of future big data analytics

To become successful in the future big data analytics landscape, you need to have the ability to derive useful information from big data. There’re different approaches to learn the key skills needed to become a data analytics professional like self-learning, learning from tutorials etc but we’d suggest you take a course in order to learn from instructors with real-world experience. Let’s have a look at the skills.

3.1- PROGRAMMING

A big data analytics professional needs to have a solid understanding of coding because a lot of customization is needed to handle the unstructured data. Some of the most used languages in the field include Python, R, Java, SQL, Hive, MATLAB, Scala, among others.

3.2- FRAMEWORKS

Familiarity and a good understanding of frameworks like Hadoop, Apache Spark, Apache Storm are needed to become a part of future big data analytics. All these technologies would help you in big data processing to a great extent.

3.3- DATA WAREHOUSING

Adequate knowledge of data warehousing is a must to become a good data analytics professional. You’ll be expected to possess a good understanding of working with database systems like Oracle, MySQL, HDFS, NoSQL, Cassandra, among others.

3.4- STATISTICS

While you’ve to have a robust understanding of the technologies used in the field, good knowledge of statistics is also a must for working with big data. Statistics is the building block of data analytics and expertise in core concepts like random variables, probability distribution etc is extremely important if you want to hold a strong position in the future big data analytics landscape.

3.5- STRONG BUSINESS ACUMEN

One of the most crucial skills to become a big data analytics professional is a solid understanding of the business domain. In fact, one of the key reasons behind the huge demand of big data analysts is that it’s highly difficult to find someone with adequate knowledge in statistics, technical skills, and business landscape. There’re professionals who’re expert programmers but don’t have the needed business acumen, and thus may not be the ideal fit for future big data analytics domain.

Final takeaway

The advent of IoT together with the developments in the AI field has simplified implementation of big data analytics to the degree that even small and medium scale businesses can benefit from them. And since almost every sector from banking and securities, education, healthcare to consumer trade, manufacturing, and energy is directly or indirectly making use of data analytics, the importance of it increases even further. As we’re moving toward a more connected future, big data analytics is going to play a major role in the future. With technologies around the world becoming more interoperable and synchronous, data will become the most important avenue that connects them together. So, it can be said that this is the ideal time to start developing the skills and become a master of them to hold a good place in the future big data analytics landscape.

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To learn more about big data, click here and read our another article.00

The post Will You Be A Part Of Future Big Data Analytics? first appeared on Magnimind Academy.

1- KNIME

KNIME Analytics Platform is one of the most popular solutions for data-driven innovation. It helps you discover the hidden potential in the data, predict new futures, or derive fresh insights. With a wide range of integrated tools, the most comprehensive choice of advanced algorithms, hundreds of ready-to-run examples, and over a thousand modules, this is one of the best toolboxes for any data analysis professional.

2- Tableau Public

It’s one of the highly effective data analysis tools with good functionalities and features. Tableau Public is considered exceptionally powerful in the business domain because it communicates insights via data visualization. It comes with a million row limit which offers a great working ground for tasks related to data analysis. With the help of Tableau’s visuals, you can chalk out a hypothesis quickly, sanity check the instinct, and start exploring the data.

3- RapidMiner

This data analysis tool works similar to KNIME i.e. through visual programming and can help you manipulate, analyze, and model data. RapidMiner helps data science teams to become more productive via an open-source platform for data preparation, machine learning, as well as, model deployment. It comes with a unified data science platform which expedites the development of complete analytical workflows in a single environment, thus improving efficiency dramatically.

4- OpenRefine

Formerly GoogleRefine, OpenRefine can help you clean, transform, and extend even messy data. This data analysis tool comes with a number of clustering algorithms that help you explore massive datasets with ease. You’d also be able to extend the data utilizing external data and web services. It supports lots of file formats to facilitate import and export.

5- R-Programming

The popular programming language comes with a software environment that can be used for statistical computing and graphics. This interpreted language supports object-oriented programming features. R is a highly popular language among data science professionals for performing data analysis and developing statistical software. Apart from data mining, it also offers linear and nonlinear modeling, statistical and graphical techniques, classification, time-series analysis, and many more.

Conclusion

While all the above-mentioned data analysis tools are designed to make your job a tad easier, they’re only as effective as the information you put in and the analysis you conduct. As business remains at the core of data analysis, you should identify your own professional inclination first before you start learning these tools. Data analysis tools aren’t only available in a huge number, they’re highly diversified as well. And that’s why it’s crucial to determine the aspect of data analysis you want to head to.

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To learn more about data analysis, click here and read our another article.

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How Python is used at every step of data analysis

How Python is used at every step of data analysis

Numpy and Pandas: Imagine staring at a long Excel sheet with hundreds of rows and columns, from which you want to derive useful insights by searching for a specific type of data in each row and column and performing certain operations. Since such tasks are extremely time-consuming and cumbersome, Python can come to your aid. With Python libraries such as Pandas and Numpy, you can use parallel processing for such high-computational tasks, which makes the job faster and easier.

BeautifulSoup and Scrapy: Using BeautifulSoup, you can parse and extract data out of XML and HTML files. On the other hand, Scrapy – which was originally designed for web scraping, can also be used as a general-purpose web crawler or to mine data using APIs. Since the necessary data isn’t always readily available, you can use these Python libraries to extract data from the internet, which would help in data analysis.

Seaborn and Matplotlib: Instead of seeing a lot of data jumbled on a screen, it’s much easier to visualize the data in the form of pie-charts, bar graphs, histograms, etc. Such pictographic representation or visualization of the data helps in deriving useful insights quickly and easily. Here again, Python libraries can come to the rescue. Using Seaborn (which is a matplotlib-based Python data visualization library) that provides you with a high-level interface for drawing informative and attractive statistical graphics, you can easily visualize data and draw useful insights. Apart from being equipped with beautiful default styles, the statistical plotting library of Seaborn is also designed to work extremely well with the Pandas dataframe objects.

In addition, using Python would mean having scikit-learn (a machine learning library), which would help in complex computational tasks involving probability, calculus, and matrix operations over thousands of columns and rows.  For data analysis involving images, OpenCV (which is an image and video processing library used with Python) can help.

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