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Tag: best data analytics course with placement

Data Cleaning and Preprocessing: Ensuring Data Quality

Posted on by Imarticus Learning

Data cleaning and preprocessing are crucial phases in data analysis that entail changing raw data into a more intelligible, usable, and efficient format. Data cleaning is repairing or deleting inaccurate, corrupted, improperly formatted, duplicate, or incomplete data inside a dataset. On the other hand, data preprocessing comprises adding missing data and correcting, fixing, or eliminating inaccurate or unnecessary data from a dataset. Enrolling in a comprehensive data science course with placement assistance helps one to enhance Power BI or Python programming skills and establish a successful career in data analytics.

data analytics course

By spending time and effort in data cleaning and preprocessing, firms can lower the risk of making wrong judgements based on faulty data. This ensures that their analyses and models are based on accurate and trustworthy information. Let’s get detailed insights from this blog.

Role in ensuring data quality and accuracy

Ensuring data quality and accuracy is critical for enterprises to make informed decisions and prevent costly mistakes. Here are several methods and recommended practices to maintain data quality:

  • Identify data quality aspects: Data quality is judged based on factors such as correctness, completeness, consistency, reliability, and if it’s up to date.
  • Assign data stewards: Data stewards are responsible for ensuring the data accuracy and quality on stated data sets.
  • Management of incoming data: Inaccurate data usually comes through data receiving. Thus, it’s essential to have complete data profiling and surveillance.
  • Gather correct info requirements: Satisfying the needs and providing the data to customers and users for the purpose the data is meant is a crucial component of having good data quality.
  • Monitor and analyse data quality: Continuously watching and assessing data quality is essential to ensure it fits the organisation’s needs and is correct and trustworthy.
  • Use data quality control tools: Different tools are available to monitor and measure the quality of data that users input into corporate systems.

Identifying and handling missing data

Identifying irregular data patterns and discrepancies is a crucial part of data cleaning. Inconsistent data can impede pivot tables, machine learning models, and specialised calculations. Here are some tips for identifying and correcting inconsistent data:

  • To make it simple to spot the incorrect values, use a filter that displays all of the distinct values in a column.
  • Find patterns or anomalies in the data that can point to errors or inconsistencies.
  • Find the cause of the inconsistencies, which needs more investigation or source validation.
  • Create and implement plans to address any disparities and prevent them in the future.

Inaccuracies in data collection, measurement, research design, replication, statistical analysis, analytical decisions, citation bias, publication, and other factors can all lead to inconsistent results. It is crucial to correctly analyse and compare data from various sources to find contradictions.

Techniques for identifying missing data

Here are some techniques for identifying missing data:

  • Check for null or NaN (Not a Number) values in the dataset.
  • Look for trends in the missing data, such as missing values in specific columns or rows.
  • Use summary statistics to locate missing data, such as the count of non-null values in each column.
  • Visualise the data to discover missing deals, such as heatmaps or scatterplots.
  • Use data cleansing and management techniques, such as Stata’s mvdecode function, to locate missing data.
  • Discuss how to address missing data with those who will undertake data analysis.

Benefits and limitations of automation in data cleaning processes

Benefits of automation in data cleaning processes:

  • Efficiency: Automation can minimise the burden and save time since cleaning can be time-consuming and unpleasant.
  • Consistency: Automated data cleaning assures reliable findings by applying the same cleaning techniques across all data sets.
  • Scalability: Automated data cleansing can handle massive amounts of data and be scaled up or down as needed.
  • Accuracy: Automation can decrease human error by swiftly finding and rectifying problems using automated data cleansing. Minimising human participation in data-collecting procedures ensures that data is inherently more high-quality and error-free.
  • Real-time insights: Automation can deliver real-time insights and more accurate analytics.

Limitations of automation in data cleaning processes:

  • Lack of control and transparency: Automated data cleaning methods could have various disadvantages, such as the lack of control and transparency when depending on black-box algorithms and established rules.
  • Not all data issues can be resolved automatically: User intervention can still be essential.
  • Over-reliance on automation can be a restriction, as automated solutions are not meant to replace human supervision.
  • Expensive tooling: A drawback of automated cleaning is that the right equipment could be costly.

Overview of tools and software for data cleaning and preprocessing

Data scientists are estimated to spend 80 to 90 % of their time cleaning data. Numerous industry solutions are accessible to speed up data cleansing, which can be valuable for beginners. Here are some of the best data-cleaning tools and software:

  • OpenRefine: A user-friendly GUI (graphical user interface) application that allows users to investigate and tidy data effortlessly without programming.
  • Trifacta: A data preparation tool that provides a visual interface for cleaning and manipulating data.
  • Tibco Clarity: A data quality tool that can assist in finding and rectifying data mistakes and inconsistencies.
  • RingLead: A data purification tool that can assist in finding and removing duplicates in the data.
  • Talend: An open-source data integration tool that can aid with data cleansing and preparation.
  • Paxata:  A self-service data preparation tool that can help automate data cleansing activities.
  • Cloudingo: A data purification tool that can assist in finding and eliminating duplicates in the data.
  • Tableau Prep: A data preparation tool that gives visible and direct ways to integrate and clean the data.

How to ensure data quality in data cleaning and preprocessing?

Here are some steps to ensure data quality in data cleaning and preprocessing:

  • Monitor mistakes and maintain a record of patterns where most errors come from.
  • Use automated regression testing with detailed data comparisons to ensure excellent data quality consistently.
  • Cross-check matching data points and ensure the data is regularly formatted and suitably clean for needs.
  • Normalise the data by putting it into a language that computers can comprehend for optimal analysis.

Conclusion

Data cleaning and preprocessing are crucial in the significant data era, as businesses acquire and analyse massive volumes of data from various sources. The demand for efficient data cleaning and preprocessing methods has expanded along with data available from multiple sources, including social media, IoT devices, and online transactions.

Imarticus Learning offers a Postgraduate Program in Data Science and Analytics designed for recent graduates and professionals who want to develop a successful career in data analytics. This data science course with placement covers several topics, including Python programming, SQL, Data Analytics, Machine Learning, Power BI, and Tableau. The machine learning certification course aims to educate students with the skills and information they need to become data analysts and work in data science. Check the website for further details.

Here’s how you can excel in a data analytics course with placement

Posted on by Imarticus Learning

Here’s How You Can Excel In A Data Analytics Course With Placement

In the digital age, most companies use advanced technology in their business which, in turn, creates a lot of data in the form of several digital footprints. Now humanly, it is not possible to comb through such digital footprints and find trends or patterns which could benefit the business.

Here, the concept of data analytics comes into play which can dig deep and provide meaningful insights that will not only help to find trends but will also help the business to grow. And, this has made business analytics courses one of the sought-after training courses for students globally.

Types of Data Analytics

There are 4 types of data analytics:

Descriptive Analytics – With the usage of key performance indicators (KPI), it answers particular questions like ‘what happened?’ and ‘what is happening?’ and gauges the success or failures of methods implemented in the business.

Diagnostic Analytics – To easily summarise, it is considered the upgraded version of descriptive analytics. It goes further digging into the raw data and provides information about ‘why it failed?’ or ‘why it succeeded?’

Predictive Analytics – From the name, it can be understood that it predicts the future outcome of any initiative by finding key patterns or trends. It also sheds light on the fact further it will again happen or not.

Prescriptive Analytics – Heavily dependent on machine learning, this process collects data from the predictive analysis and provides insights on ‘how to get the work done?’ and is a great way to avoid rash decisions. 

What advantages would you have with a certification in data analytics?

The processes that are involved in data analytics are data mining, data management, statistical analysis, and data presentation. After learning these processes, your abilities would include: 

Firstly, the omission of guesswork which, in turn, will help to plan proper designs for various business models.

Providing tailor-made customer service is one of the key strategies for a successful business. With its cutting-edge technology, it would analyze the interests and concerns of the customers and would recommend in the same manner creating a trustable customer-company relationship.

With proper information on the table, it would benefit one by cutting the budget and saving valuable time. Both of these precious elements can be invested in other places for further development.

The leads which were once lost in the tons of data now can be easily converted into potential customers, and also, it is one of the most demanding professions in the world according to reports by Forbes.

The sectors that have implemented data analytics are: 

Retail Sector – By using data analytics, retailers understand the trends and the needs of their customers. And then, supply them with their want hence increasing their profit.

Financial Sector – There has been an extreme rise in loan scams and frauds globally. In the financial sector, data analytics has been a blessing that has helped to curb these scams to a great extent.

Logistics – Data analytics helps to provide efficient safe routes which, in turn, helps the companies to deliver desired materials in less time conveniently.

Healthcare – It has had a great impact on the healthcare sector, as it not only helps to develop new methods for preparing drugs but also helps in accurate diagnostics of patients and thus providing them with proper treatment.

Conclusion

With the advancement of technology, a career in data analytics seems to be a smart choice, and for doing so, you can definitely check out data analytics certification courses online designed by Imarticus Learning. The course is implemented with real business projects, case studies and mentorships which will help you excel in the corporate world.