{"id":98221,"date":"2020-10-25T02:42:25","date_gmt":"2020-10-25T00:42:25","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/kak-stat-uspeshnym-data-sajentistom-i-analitikom-dannyh"},"modified":"2020-11-18T00:58:46","modified_gmt":"2020-11-17T22:58:46","slug":"kak-stat-uspeshnym-data-sajentistom-i-analitikom-dannyh","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/administrirovanie\/kak-stat-uspeshnym-data-sajentistom-i-analitikom-dannyh","title":{"rendered":"How to Become a Successful Data Scientist and Data Analyst","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\"How<\/a><\/noindex>
\nThere are many articles about the skills needed to be a good data scientist or data analyst, but only a few discuss the skills necessary for success\u2014be it exceptional performance reviews, recognition from management, promotions, or all of the above. Today, we present material from an author who wishes to share her personal experience working as a data scientist and data analyst, as well as what she has learned to achieve success.
\n<\/a><\/noindex><\/p>\n

I was fortunate: I was offered a position as a data scientist when I had no experience in Data Science. How I managed this task is another story, and I must say that I had only a vague idea of what a data scientist does before I accepted this job. <\/p>\n

I was hired to work on building data pipelines due to my previous experience as a data engineer, where I developed a data warehouse for predictive analytics used by a group of data scientists. <\/p>\n

My first year as a data scientist involved creating data pipelines for training machine learning models and deploying them into production. I stayed in the background and participated in very few stakeholder meetings in marketing, who were the end users of the models. <\/p>\n

In my second year at the company, the data processing and analysis manager responsible for marketing left. Since then, I became the primary player and took a more active role in model development and discussing project timelines.<\/p>\n

As I communicated with stakeholders, I realized that Data Science is a vague concept that people have heard of but do not fully understand, especially among senior management.<\/p><\/blockquote>\n

\nI built over a hundred models, but only one-third of them were used because I did not know how to demonstrate their value, despite the fact that the models were initially requested by marketing. <\/p>\n

One of my team members spent months developing a model that, according to senior management, would demonstrate the value of the data science team. The idea was to roll this model out across the organization after its development and encourage marketing teams to apply it.<\/p>\n

This turned out to be a complete failure because no one understood what a machine learning model was and couldn\u2019t grasp the value of applying it. As a result, months were wasted on something nobody wanted.<\/p><\/blockquote>\n

\nFrom such situations, I've gleaned certain lessons which I will outline below.<\/p>\n

Lessons I've learned to become a successful data scientist<\/h2>\n

\n1. Set yourself up for success by choosing the right company.<\/b>
\nDuring your job interview, ask about the data culture and how many machine learning models are typically used in decision-making. Request examples. Find out if the data infrastructure is set up for modeling. If you spend 90% of your time trying to extract raw data and clean it, you\u2019ll have little time left to build models that demonstrate your value as a data scientist. Be cautious if you are the first data scientist hired. This can be good or bad, depending on the data culture. You may face significant resistance when implementing models if senior management hires a data science specialist just because the company wants to be known as using Data Science for better decision-making<\/i>, but has no idea what this actually means. Additionally, if you find a company that is data-driven, you will grow alongside it.<\/p>\n

2. Know your data and key performance indicators (KPIs).<\/b>
\nIn the beginning, I mentioned that as a data processing engineer, I created an analytics data warehouse for the data science team. Once I became a data scientist myself, I was able to identify new opportunities that improved the accuracy of models because I intensely worked with raw data in my previous position. <\/p>\n

By presenting the results of one of our campaigns, I was able to demonstrate models generating higher conversion rates (in percentage), after which one of the KPI campaigns was measured. This showcased the model's value for business effectiveness, which can be linked to marketing.<\/p>\n

3. Ensure user adoption of the model by demonstrating its value to stakeholders.<\/b>
\nYou will never succeed as a Data Science specialist if stakeholders never apply your models for business decision-making. One way to ensure adoption is to find a business pain point and demonstrate how the model can help. <\/p>\n

After speaking with our sales department, I realized that two representatives work full-time, manually sifting through millions of users in the company database to identify single-license users who are more likely to convert to team licenses. A set of criteria was applied in the selection process, but it took a lot of time because representatives reviewed one user at a time. With the model I developed, representatives were able to identify users with the highest likelihood of purchasing a team license, thereby increasing the conversion rate in less time. This led to more efficient use of time with improved conversion rates for key performance indicators related to the sales department.<\/p>\n

Several years passed, and I repeatedly developed the same models and felt that I was no longer learning anything new. I decided to look for a different position and eventually got a job as a data analyst. The difference in responsibilities couldn't have been more significant compared to my time as a data scientist, even though I was once again supporting marketing.<\/p>\n

This was the first time I analyzed A\/B experiments and discovered all<\/i> the ways in which an experiment can go wrong. As a data scientist, I didn\u2019t work on A\/B testing, as it was reserved for the experimentation team. I dealt with a wide range of analytical studies influenced by marketing\u2014from enhancing premium conversion rates to user engagement and churn prevention. I learned many different ways to view data and spent considerable time compiling results, presenting them to stakeholders and upper management. As a data scientist, I primarily worked on one type of model and rarely presented findings. Fast forward a few years to the skills I learned to be a successful analyst.<\/p>\n

Skills I Learned to Become a Successful Data Analyst<\/h2>\n

\n1. Learn to Tell Stories with Data<\/b>
\nDon\u2019t look at KPIs in isolation. Connect them; view the business as a whole. This will help identify interrelated areas. Upper management views the business through this lens, and a person demonstrating such skills is noticed when it comes time to make promotion decisions.<\/p>\n

2. Provide Actionable Insights<\/b>
\nOffer the business an actionable insight<\/i> to address an issue. Even better, proactively propose a solution before it\u2019s flagged as a pressing concern. <\/p>\n

For example, if you told marketing: \"I noticed that website visitors have been decreasing monthly lately,\"<\/i>that\u2019s a trend they might have seen on the dashboard, and you wouldn't have offered any valuable insight as an analyst because you merely stated an observation. <\/p>\n

Instead, analyze the data to find the cause and propose a solution. A better example for marketing would be: \"I noticed that our website visitors have decreased recently. I found that the source of the problem is organic search, due to recent changes that caused a drop in our Google ranking.\"<\/i>This approach shows that you\u2019ve been tracking company KPIs, noticed a change, investigated the cause, and proposed a solution to the problem.<\/p>\n

3. Become a Trusted Advisor<\/b>
\nYou need to become the first person your stakeholders turn to for recommendations or questions regarding your area of expertise. There\u2019s no shortcut, as demonstrating these skills takes time. The key is to consistently provide high-quality analysis with minimal errors. Any mistakes in your calculations will cost you credibility, as next time you present an analysis, people may wonder: If you were wrong last time, could you be mistaken this time too?<\/i>. Always double-check your work. It also doesn\u2019t hurt to ask your manager or a colleague to review your numbers before presenting them if you have any doubts about your analysis.<\/p>\n

4. Learn to communicate complex results clearly<\/b>
\nAgain, there\u2019s no shortcut to learning effective communication. It takes practice, and over time, you will get better at it. The key is to identify the main points of what you want to accomplish and recommend any actions that stakeholders may take to improve the business based on your analysis. The higher you are on the corporate ladder, the more important communication skills become. Conveying complex results is a crucial skill that must be demonstrated. I spent years learning the secrets to success as a data scientist and data analyst. People define success differently. Being labeled as an 'amazing' and 'star' analyst is how I see success. Now that you know these secrets, I hope your journey leads you to success faster, however you define it.<\/p>\n

To make your path to success even quicker, keep this promo code HABR<\/b>, which can give you an additional 10% discount on the price indicated on the banner.<\/p>\n

\"How<\/a><\/noindex><\/p>\n