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Tag: postgraduate program in data science and analytics

Leading Skills for Data Science Experts

Posted on by Imarticus Learning

In today’s age of technological innovation and digitisation, data is undoubtedly one of the most important resources for an organisation. It is one of the most crucial prerequisites for decision-making. Reports estimate that as much as 328.77 terabytes of data are generated on a daily basis. This has, in turn, led to an exponential growth in the demand for data scientists who can actually analyse the vast amount of data and use it for business purposes. 

Data Science Course

Some of the many industries that have caused such a high data scientist job demand include retail businesses, banks, healthcare providers, and insurance companies, among others. In order to succeed in this field, you need to have more than just a basic familiarity with code. 

This brings us to the question, what are the most important skills required to become a data science expert?

Let’s find out!

What is A Data Scientist?

Before delving into the details of the leading skills for data scientist experts, let’s first understand what is a data scientist and their roles and responsibilities. 

Simply put, a data scientist is a professional whose primary goal is to solve complex problems and make crucial data-driven decisions. They are responsible for analysing large and complex data sets in order to identify patterns, understand trends, and find any correlations that can help organisations gain valuable insights. 

The responsibilities of a data scientist may vary based on the organisation or the type of business they work for. Nonetheless, listed below are some of the most basic and common responsibilities that every data scientist is expected to fulfil.

  • Collaborating with different departments, such as product management, to understand the needs of the organisation and devise plans accordingly, 
  • Staying up-to-date with the latest technological trends and advancements
  • Applying statistical analysis methods and machine learning algorithms to derive insights from data
  • Identifying and engineering relevant features from data to enhance both the accuracy and effectiveness of models
  • Evaluating the performance of models using various metrics and validation techniques
  • Effectively communicating any valuable insights to stakeholders and non-technical audiences.
  • Exploring and visualising data via multiple statistical techniques and visualisation tools

Skills Required To Be A Data Scientist

The skills required to be a data science expert can broadly be divided into two types. They are, namely,

  • Technical skills and 
  • Non-technical skills

Technical Skills

Mentioned below are a few technical skills that every data science expert must possess

Programming: In order to excel in this field, you must have an in-depth knowledge of the crucial programming languages and not just Python. Such include C/C++, SQL, Java, and Perl. This will help you to organise unstructured data sets in an efficient manner.

Knowledge of Analytical Tools: Having a thorough understanding of the various analytical tools and how each of them operates is also a must for a data science expert. Some of the most commonly used tools include SAS, Spark, Hive and R, among others. 

Data Visualization: Data visualisation skills are important for communicating insights effectively. This includes proficiency in various visualisation libraries and tools such as Power BI and Tableau. All these facilitate the creation of interactive and visually appealing visualisations.

Data Mining and Text Mining: A deep understanding of various data mining techniques, such as clustering, or association rules, can also prove to be extremely useful, especially for uncovering hidden patterns and relationships in data. Additionally, you are also required to possess text mining skills such as natural language processing and sentiment analysis to be able to extract valuable insights from unstructured text data.

Non-Technical Skills

Non-technical skills also referred to as soft skills, are as crucial as technical skills. Therefore, they should never be ignored. Here are some of the most important non-technical skills you must possess in order to be successful in this field.

Communication: The nature of this field is such that it requires you to communicate with various departments and individuals on a daily basis. Therefore you must possess excellent communication skills so that you can communicate your ideas and thoughts to different team members in an efficient and precise manner. 

Strong Business Acumen: Understanding the business context and organisation goals is crucial for every data science expert. You must be able to align all the data science initiatives with business objectives while simultaneously providing actionable insights that will add some sort of value to the overall business. 

Analytical Thinking: Other than these, a data science expert must also possess strong analytical thinking abilities. In this manner, you can approach any given problem in a logical and structured manner. You must be able to break down any large and complex issue into smaller and simpler subsets, analyse them individually, and design innovative solutions for the same.

 Adaptability: The field of data science is continuously evolving, with innovations and advancements happening every day. Therefore, as a data science expert, you must possess the ability to embrace these new changes and stay up to date with the latest innovations in technologies, methodologies or approaches. In this manner, you will always remain one step ahead of your competitors and eventually gain success.

Conclusion

While all these technical and non-technical skills are crucial for being successful as a data science expert, you are also required to have a strong educational background. This includes a Master’s degree or a PhD in computer science, engineering, statistics, or any other related field. Additionally, you can also opt for specialised courses that are designed to train students who wish to pursue a career in data science. 

One such includes the Post Graduate Program in Data Science and Analytics offered by Imarticus Learning. It is specifically designed for fresh graduates and professionals who wish to develop a successful data science career. With the help of this course, you can gain access to real-world applications of data science and explore various opportunities to build analytical models that enhance business outcomes. Additionally, you also get to enjoy several other benefits, such as career mentorship, interview preparation workshops, and one-on-one career counselling sessions, among others. 

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.

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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.