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Digitalisation has become a part of our everyday lives, leading to enormous data generation. Proper usage of it helps organisations to derive valuable insights. Data analysts and scientists are the key people who can derive valuable information from such data. As a result, their demand is growing.

Digitalisation has become a part of our everyday lives, leading to enormous data generation. Proper usage of it helps organisations to derive valuable insights. Data analysts and scientists are the key people who can derive valuable information from such data. As a result, their demand is growing.

The creative position next to a data analyst is a data scientist. So, every data analyst must aspire to reach the position to experience growth. Data analyst is, of course, the initial position to acquire. Once you have gained the required expertise in performing data analytics, it is time to progress as a data scientist.

Herein, we will know the best processes to transit from a data analyst to a data scientist. As part of this, we will cover some significant points :

What is data analytics?

Data analysts collect and analyse data to draw meaningful insights, which will help make effective decisions. Raw data is not practically helpful. However, with data analysis, the data can become meaningful.

Data analytics identifies various patterns of the datasets to derive valuable and relevant information. For instance, you can know about customer behavior, employee engagement, the scope of industry growth, etc. Moreover, the insights help predict future trends through informed decisions eliminating any guesswork.

What is data science?

Data analytics is one branch of data science. Data science analyst work on both large datasets of structured and unstructured data. Mainly, they focus on what data to collect and how to collect it, proving that they require a more profound understanding than data analysts, who are just responsible for making conclusions from the existing data.

Why grow as a data scientist? 

Despite the varied complexities the data scientist position carries, choosing a career as a data scientist is an effective option for the data analyst. Let us know why?

Demand: Data scientists are in demand. In 2018, LinkedIn generated a report that stated a shortage of 151,717 data scientists in the US alone. Thus, if you choose to grow in data science, it is time to do so.

Well paid: Data scientists are well paid. Payscale, a salary comparison site, revealed that data scientists are paid about $67K to $134K per year in the US.

Add value: Data science analyst form the core element of the business strategy. The top-level of any organization values their decisions. On the other end, data analysts have limited functioning say they deal with a single domain such as marketing and sales. Hence, what a data scientist dedicates his skillset to any organization is of significant value.

Evolving: Data science is evolving. The role of a data scientist differs with the organization. Hence, something interesting awaits to learn and evolve. May it be data architecture designing or creating new algorithms, the job of a data scientist is ever exciting and creative.

Need: Organizations have understood the importance of data analysis. Consequently, the need for data science analyst has been growing. E-commerce, healthcare, sports, finance, etc., are the few domains actively incorporating data analysis.

Data scientist’s skillset 

Assessing the below fundamental skill sets is essential before acquiring the skill set of a data science analyst.

  • Experience in Python and R.
  • Relational database experience
  • Experience in collecting and cleaning data
  • Create visualisations

Apart from this practical knowledge, soft skills such as critical thinking, problem-solving, presentation skills, analytical skills, etc., must be cultivated.

In addition to data analyst expertise, a data scientist must possess advanced technical abilities, a practical mindset, a deeper understanding of statistics. The below list mentions a few technical requirements:

  1. Data languages: Advanced Python and R

  2. Machine Learning: SVM, KNN, decision tree, random forest, linear and logistic regression, etc.

  3. Relational databases: Oracle, Microsoft SQL Server, MySQL, PostgreSQL

  4. Data visualisation: Matplotlib, ggplot, R shiny, etc.

  5. API tools- Microsoft Azure, IBM Watson, and OAuth

  6. PG qualification: Master’s or Ph.D. in statistics, computer science

Conclusion: 

Data science analyst is a welcoming job for creative and analytical people. Of course, one needs to acquire the required technical expertise. Undoubtedly, the above information helps data analysts realize their dream as data scientists.



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