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Data Scientist Careers – How to Grow in Your Data Scientist Job in 2022

We are quite sure that you have come across the terms like Data Scientist, Data Analyst and more in recent days. The question is what is a data scientist? The world revolves around “data”. Every company in every country gathers and analyses data about their customers. Why? To ensure better service! It is as simple as that. A data scientist career or a data analyst career is the most sought-after career these days. In today’s digital world, we can gather humongous volumes of data that require non-traditional processing methods. This is where the data scientist job comes in.

Who is a Data Scientist?

A data scientist’s job is to analyse and interpret data. He specializes in that. They make use of data science skills to help businesses in taking better decisions and make their operations efficient. When it comes to the skills needed to become a data scientist, you need to have a strong background in mathematics, computer science and statistics. They use their data science skills to analyze large data sets to find trends or patterns. Also, a data scientist can develop new ways to store and collect data.

What are the Data Scientist Qualifications Required?

When it comes to data scientist qualifications, you need strong mathematical and analytical skills. If you want to opt for a data scientist career, you must be able to work with complicated data sets. Also, a data scientist job will need you to use statistical software packages. You must be familiar with the programming languages like Python. It will help your data scientist career if you have a certification from an accredited program.

A strong background in mathematics and computer science is crucial as a part of a data scientist qualification. You have to work with huge volumes of data every day. Therefore, you must have a strong knowledge of mathematics and computer science. Next on the data scientist qualifications list is the experience in statistical modelling and machine learning. These are the two powerful tools that a data scientist job needs you to use daily. Therefore, if you want to build your data scientist career, you must have experience with these techniques.

data science prerequisites

Source – Dataflair

Strong visualization and communication skills are a must. As a data scientist, you should be able to effectively communicate your findings to others. Therefore, strong communication and visualization skills are crucial for anyone interested to become a data scientist.

How Can You Become a Data Scientist?

Data science is the area of study which involves extracting knowledge from all the gathered data. There is a huge demand for professionals who can analyse and turn data into a competitive advantage for their organization. When you opt for a data scientist career, you shall create data-driven business solutions and analytics.

7 Top Skills of a Data Scientist

To flourish in your data scientist job, you must master the following skills:

Skill 1

The first skill of a data scientist is to garner the database knowledge needed to store and analyse data through multiple tools like Oracle, MySQL, Database, Microsoft SQL Server and Teradata.

Skill 2

To thrive in your data scientist career, you have to learn statistics, mathematical analysis and probability. Statistics includes developing and studying methods to collect, analyze, interpret and present empirical data. Probability deals with the measure of the likelihood that an event is going to occur. Mathematical analysis is that specific branch of maths that deals with limits and other related theories. These include integration, infinite series, differentiation and analytic functions.

Skill 3

One of the most crucial skills of a data scientist is to master at least one programming language. Therefore, you have to learn programming tools like Python, R and SAS because you will need them for data analysis. R is a free software tool that you need to learn for statistical computing and graphics. This supports most of the machine learning algorithms for data analytics like association, regression and clustering.

On the other hand, Python is an open-source general-purpose programming language. Python libraries SciPy and NumPy are used in Data Science. Also, learning SAS can help you to manage, alter and retrieve data from a wide array of sources and perform statistical analysis of the data.

Skill 4

You also have to learn about data wrangling. This includes cleaning; organizing and manipulating data The popular tools for data wrangling include Flume, Scoop, Python and R.

Skill 5

Mastering the concepts of machine learning is important for a data scientist career. Machine learning is offering systems the ability to learn automatically and also improve from the experience without being programmed to. You can learn machine learning through a wide array of programs including Naive Bayes, Regressions, SVM, KNN, K Means Clustering and Decision Tree Algorithms.

Skill 6

When it comes to the skills of a data scientist, if you have the working knowledge of Big Data Tools, it comes in handy. Therefore, you must know Hadoop, Apache Spark, Talend, and Tableau because these are used to deal with large and complicated data. You can’t do this without using traditional data processing software.

Skill 7

Finally, you must develop the capability to visualize the results. Data science skills include data visualization and integrating different sets of data. You must create a visual display of the results through charts, diagrams and graphs. When it comes to mastering the data scientist career, you have to understand and also know how to work with the core technologies that analyse big data. In the above section, we have mentioned the skills you will need to flourish in your data scientist job. Let us share a detailed overview of why you will need those skills to prepare for jobs in data science.

data science job

Source – Towards Data Science

1. Learning Python

If you want to become an expert data scientist and create a good data scientist career, then you have to learn Python. This is the most common coding language used by data scientists. Since it is very easy to use and it also offers a powerful library (like NumPy, SciPy, and Pandas) useful for data analysis, it is so much preferred for the career of a data scientist.

2. You Must Learn Statistics

Whether you call it a milestone for a data scientist’s career path or the skill of a data scientist, statistics is the grammar of data science. Do you want to know why? Statistics include the method to analyse and interpret large sets of data. When it comes to data analysing and getting insights, statistics is as important as air. If you want to figure out the hidden details from large datasheets, then you must have a deep learning of statistics.

3. Experience in Data Collection

This is a crucial element in the field of data science. The knowledge of data collection comes with experience. Therefore, you have to know the use of different tools to import data from local systems like CSV files. Also, you must be aware of how to scrap data from websites through the beautifulsoup python library. Data scrapping could also be API-based. You can also manage data collection with the knowledge of ETL pipelines in Python or Query Language.

4. Data Cleaning

This is where you have to spend most of your time as a data scientist. Data cleaning is about gaining data, which is fit for doing work and analysis, missing values, removing unwanted values, outliers, categorical values and wrongly submitted records from the Raw form of data. Data cleaning is crucial because the real-world data is messy and attaining it with the help of a wide array of Python libraries (Numpy & Pandas) is crucial for the data scientist career.

5. A Detailed Knowledge of Deep Learning

Machine learning is the core skill needed to become a data scientist. It is useful for building different predictive models, classification models and more. The best part is that companies optimize their plan according to the predictions. On the other hand, deep learning is the advanced version of machine learning that uses a neural network. This is a framework that combines different machine learning algorithms to solve different tasks to train data. The different neural networks include recurrent neural networks (RNN) and convolutional neural networks (CNN).

6. Learning of ML Model

Deployment is the process of making the process of the machine learning model available to the end-users. You can achieve this with the integration of the model with a lot of existing production environments. Therefore you can implement the practical use of the ML model for different business solutions. There are a lot of services that deploy machine learning models like MLOps, Flask, Microsoft Azure, Cloud, Google, Heroku, and more.

The life of a data scientist

Source – The College Post

Career Options as a Data Scientist

In the above section of the blog, we have compiled a list of skills needed for a data scientist career. Once you have mastered the above skills, you shall have a wide array of opportunities available. In the following section of this blog, we will share the different types of career options that you can choose from.

1. Data Scientist

This is the most popular and preferred option for anyone studying data science. Data scientists create a data-driven business solution by driving optimization and improvement in product development. They make use of predictive modelling for increasing and optimizing consumer experiences, revenue generation, ad targeting and more. Data scientists coordinate with a lot of functional teams for implementing different models and monitoring their outcomes. The average Data Scientist’s salary is $120,931.

2. Data Engineer

The work of a data engineer is to assemble complex data sets. They design, identify and implement internal process improvements and then create the infrastructure needed for data extraction, loading and transformation in the best way. They also help to build analytics tools that use the data pipeline. The average salary of a data engineer is $137,776.

3. Data Architect

The work of a data architect is to analyse the structural needs for new software and applications and also develop database solutions. They configure & install the information systems and migrate data from the legacy systems to the new ones. The average salary of a data architect is $112,764.

4. Data Analyst

The work of a data analyst is to acquire data from secondary or primary sources and maintain the databases. They interpret the data, analyse results through statistical techniques and also develop data collection systems that can help the management to prioritize business and information. The average salary of a data analyst is $65,470.

5. Business Analyst

A business analyst’s job is to assist a company with monitoring and planning through eliciting and organizing requirements. They ensure every resource is validated and also create cost-estimate models with actionable, informative and repeatable reporting. The average salary of a business analyst is $70,170.

6. Data Administrator

Data administrators assist in database design and also update the existing databases. They are the ones responsible to set up and test new database and data handling systems, ensure the security and integrity of the databases and also create complex query definitions that allow data extraction. The average salary of a data administrator is $54,364.

Are you wondering whether a data scientist career is a high-paying job or not? From the above details on the types of data scientist jobs and their salaries, we can clearly say that a data scientist career is a well-paid job.

What are the Common Tasks that a Data Scientist Perform?

Some common tasks that a data scientist performs include organizing and cleaning data, creating data visualizations, and running statistical analyses. Additionally, they might be responsible for creating predictive models and conducting research.

What are the Career Prospects of a Data Scientist?

The demand for data scientists is increasing rapidly and also, and the career prospects are great. Data scientists along with their necessary skills and experience can obtain jobs in multiple industries that include finance, retail, healthcare and manufacturing. All of us know these days, that data is generated every second at a massive rate. Therefore, to process so much data, big firms are hunting for expert data scientists. This will help them to gain valuable insights and use them in many business strategies.

career of a data scientist

Source – Data Science Central

What are the Common Challenges that Data Scientists Face?

There are certain challenges that data scientists face. These include organizing and cleaning data sets, running the statistical analysis and also creating data visualizations. Also, they are the ones responsible for conducting research and building predictive models. These are some of the challenges that a data scientist faces in his/her career.

How to Prepare for a Career in Data Science?

The best way to prepare for a data scientist job is to opt for an integrated program in data science and AI. You should opt for the following courses to prepare for a career in data science.

  1. Data science with Python

  2. Deep Learning

  3. Machine Learning

  4. Computer Vision

  5. Tableau

Some Non-Technical Skills Needed for a Data Scientist

The non-technical skills include the following:

  1. Teamwork

  2. Communication skills

  3. Task management

  4. Business acumen/understanding

In Conclusion

You cannot deny that the study and use of data science are on the rise. Also, more and more companies are recruiting data scientist experts. Therefore, if you want to enhance your data scientist career, it is crucial to learn the above skills and update yourself. Data is the key for companies to understand their clients. Therefore, a data scientist helps a company to acquire customers by analysing their needs. This is what allows companies to make the best use of their resources.

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