The constant upswing in the hiring craze has escalated emerging job seekers to comprehend the prominence of acquiring such skills. As the domain promises a fascinating career progression for beginners as well as working professionals, learning these skills would help you earn a six-digit figure salary package.

Top 10 Data Science Skills That Will Land You A 6 Digit Job

1. Become a Pro in Statistics and Probability 

To develop top-notch mathematical models, aspiring job-seekers should have a critical understanding of topics such as statistics and probability. These are the scientific tools that help the transformation of raw and unstructured data into exemplary conclusions.

Experts believe that it is essential to discern the potential of these topics since Machine Learning and Algorithm configuration are integral parts of Data Science jobs.

2. Expertising the Programming Skills

Data science requires a strong understanding of critical coding. In order to thrive in this domain, aspirants need to become proficient in programming languages. Different programming languages such as Python, R, and Javascript are broadly exploited in creating comprehensive data science models.

3. Command over Automated Analytics Tools

Your competence to exploit Automated Analytics Tools is one of the prominent data science skills that improvise constantly. This allows techies to utilize the results of their data science mechanisms and explore the data. The several processes that computerize data differ in perplexity. The aspirant needs to understand the data analytics tools such as Whatagraph, Darwin, DataRobot, Datapine, and SAS Visual forecasting to get command over Automated analytics.

4. Data Visualisation

Data Visualisation can help aspiring data scientists how to bridge data with the ultimate consumers effectively. However, the skill is one of the most critical aspects of data analysis. It is essential to impart information or data in a way that is both comprehensible and pleasing to the eyes. These skills further help to convey stories by illustrating data in a form of easier-to-comprehend outliers and trends.

5. Good at Data Wrangling

Data wrangling has emerged as one of the most prominent concepts of data science. By mastering the skill, aspiring data scientists will be able to eliminate corrupted data and categorize it accordingly. Further, the data could be exploited for several analytical objectives. 

6. Proficiency in Software Engineering Principles

Data scientist professionals need to have a thorough knowledge of software engineering principles. They should have expertise in creating top-quality code that will ease the process during production. In addition, the concept helps aspirants with comprehensive information about the fundamental mechanism of software development data types, compilers, projects, and more. 

7. Pro in AI and Machine Learning Skills

Mastering these skills will help an aspiring data scientist’s job easier. Artificial Intelligence and Machine learning models are broadly exploited in various industries and encourage data scientists to work effectively and quickly. Nevertheless, the greatest challenge would be to figure out the right data prior to developing an AI model to carry out human tasks.

8. Strong Data Intuition

This is probably one of the most prominent data scientist skills. The aptitude of accessing lucrative data insights makes professionals more efficient at their work. However, one can gain these skills with experience, and boot camps or workshops are a great way to master them.

9. Great Business Acumen

Along with strong Technical skills, data scientist professionals must possess great business acumen. With the help of strong business acumen, professionals can easily discern the potential challenges, which might hamper the progress of the organization.

10. Ability to handle Unstructured and Large datasets

Aspiring Data Scientists are required to possess great experience with handling Unstructured and large datasets that have been received from various sources and channels. The main responsibility would be to create the data through analysis, and with the expanding amounts of data, jobseekers should understand how to pragmatically handle a massive chunk of datasets and organize them for extracting important insights. 

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Before you ring up the curtain on this rapidly growing career path, the first thing is you should be confident in your skill set. However, if you are a hardcore technologist and a coder, who is interested in how we visualize data and communicate with the data, you will find several opportunities within a single industry.  

Your resume plays an essential role in fetching the best data science job. Your resume should present all the relevant skill-set in front of the employer. Have a mention the projects that you have handled with brevity and clarity. As a whole, your resume should be crisp and clear enough to impress the employer. However, you need to keep your resume updated so that there will be more chances of getting an opportunity. 

5 Tips to Find the Right Data Science job?

A little experience could be advantageous

Securing a data science job as a fresher is not an easy task. Many aspirants carry technical and communication skills, but no prior work experience prohibits them from finding a well-paid data science job. When a candidate starts looking for a job, one has to seek relevant projects to work on concurrently. Your work experience will be considered, and your portfolio will be valued.

Channelized networking through multiple hiring platforms 

There are many networking applications to get connected to employers. For instance, LinkedIn is one of the best applications to seek a job and highlight your skill set. If you have any plans to use LinkedIn to network for a job, start building your relationship with employers. It is up to you how you start building connections. Attending conferences and meetings is a top-notch approach to perfectly channelling your networking. 

Don’t give upon data roles opportunity

Many aspirants only aim for data science roles and they hesitate to take on data-related job roles. We recommend aspirants who are looking for Data science job opportunities to take up data roles if it comes first. A data scientist role includes working with data, so it will be easy to move into a data science job later or other data-relevant jobs. 

Data-related jobs help in enhancing your domain knowledge. It will boost the data skills that are required to get into data scientist job roles. 

Try for emerging companies

The actual part of the job search is sending an application to the right company where you can transform your career. Usually, aspirants run behind well-established companies. But the fact is companies conduct a well-organised hiring process making it hard to clear the rounds for the aspirants with no relevant experience. 

However, to enhance your skill-set, you can begin your career in an emerging company. There are numerous advantages of starting your career in emerging companies because you can easily communicate with the founders of the company and your skills and contributions will be much more visible valued and appreciated. 

Apply for multiple companies

Once you become an expert in a programming language and understand the various machine learning algorithms, it is recommended to send as many applications as you can to multiple companies. Make sure that every application explains your skill set.

Read: Top 20 Frequently Asked Data Science Interview Questions 2022

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After some basic Data Science interview questions, we have included some technical and Data analysis questions that further help you crack an interview. 

Most Asked Data Science Interview Questions

1. What is Data Science?  How it is different from Big Data? 

Data Science is an interdisciplinary field that blends several tools, algorithms, and machine learning principles to with the aim to find common patterns and assemble realistic insights from the raw data using mathematical and statistical approach is called Data Science.

How Data Science is different from Big Data?

2. List the major differences between Supervised and Unsupervised Learning? 

Read: Who is a Data Scientist, a Data Analyst and a Data Engineer

3. How Data Analytics is different from Data Science? 

• Data Science is responsible for transforming data with the help of various technical analysis approaches to exploit required insights using which data analyst employ to thier different business solutions. 
• Data Analytics involves the task of examining the existing hypothesis and information and helps in answering the questions to provide effective business related decision making process.

4. Mention some of the techniques used for sampling. 

It is highly challenging task to conduct Data analysis on a whole volume of data at a time specifically when it includes larger datasets. 

It becomes essential to collect some data samples that could be used for illustrating the whole population and later carry out analysis on it.  

Notably, there are two different methods of sampling techniques based on the utilization of statistical models.

1. Probability Sampling Techniques: 

• Clustered sampling.
• Simple random sampling. 
• Stratified sampling. 

2. Non-probability Sampling Techniques: 

• Quota sampling.
• Snowball sampling.
• Convenience sampling etc. 

5. Brief the steps involved in making a decision tree.

Making decision tree includes the following steps: 

1. Get the list of entire dataset as input which are helpful for making a decision tree. 

2. Evaluate entropy of the target variable and predictor attributes. 

3. Evaluate the information gain of total attributes. 

4. Select the attribute along with the highest information gain as the root node. 

5. Reiterate the same approach on each branch until the decision node of every branch is concluded. 

6. How Data Scientists check for data quality? 

Some of the terms utilized to check data quality: 

Integrity. 

Uniqueness. 

Accuracy. 

Consistency. 

Completeness. 

7. Explain in brief about Hadoop.

Hadoop is a an open-source processing platform that handles data processing and storage for big data applications built on pooled systems.

Hadoop handles the task of splitting files into separate large blocks and directs them across nodes in a cluster. It then shifts a packs of code to nodes to execute the data in parallel. 

8. What is the abbreviation of ‘fsck’?

‘fsck’ abbreviation stands for ‘file system check’. It performs handling the task of searching for possible errors in the file. 

9. What are the conditions for Overfitting and Underfitting?

Overfitting: The Overfitting model process the simple training data. Incase any new data employed as input to the model, it fails to give any output. These conditions result owing to low bias and high variance in the  model. Decision trees are more vulnerable to overfitting. 

Underfitting: In underfitting, the model will be so simple that it is fails to find out the exact relationship in the data, and hence it does not execute well on the test data. This can take place due to high bias and low variance. Linear regression is more vulnerable to underfitting. 

10. Explain about Recommender systems? 

Recommender systems are a subdivision of information filtering systems, utilized to analyse how consumers would rate particular objects such as music, movies and more. 

Recommender systems filter large filter huge chunk of information based on the data fecilitated by a user and other factors, and they also manage user’s preference and interest. 

11. Explain differences between wide and long data formats.

Categorical data are always grouped in a wide format. 

The long format is in which there are a number of instances with several instances with many variables and subject variables. 

12. How much data is required to get a valid outcome?

All the industries are different and evaluate in different ways. Thus, they never have enough data. The amount of data which is essential depends on the approaches users use to have an best chance of procuring vital results.

13. Explain Eigen values and Eigen vectors.

Eigenvectors are known as unit vectors or colomn vectors whose length to magnitude ratio is 1.

Eigenvalues are coefficients that are implied on eigenvectors that assign these vectors different values for length or magnitude. 

14. Explain about power analaysis. 

Power analysis enables the determination of the sample size essential to find out an effect of a given siz with a assigned degree of confidence. 

15. Explain logistic regression. Mention any example related to logistic regression. 

Logisti regression is also called as the logit model. It is a approach to forecast outcome from a linear combination of variables.

For instance, let’s say that we would like to forecast the outcome of elections for a specific political leader. Therefore, we need to search whether this politician has the potential to win the election or not. Hence, the outcome would be binary that is win (1) or loss (0). 

16. Exaplain Linear Regression. Mention some of the disadvantages of the linear model.

Linear regression is an approach in which the score of variable Y is calculated with the help of a predictor variable X. Y is known as the criterion variable. 

Some of the disadvantages of Linear Regression are: 

• The assumption of linearity of errors is a major setback. 
• Overfitting problems are present which are difficult to solve. 

17. Explain Random forest model and steps to build it.

A random forest is created with the help of many number of decision trees. If you distribute the data into several different packages and build a decision tree in each of the different groups of data, the Random forest includes all those trees together. 

Steps to create a random forest model:

1. Randomly choose ‘k’ features from the sum of ‘m’ features provided k<<m.

2. Out of the ‘k’ features, predict the node D with the help of split point. 

3. Use the best split to divide the node into daughter nodes. 

4. Reiterate the steps two and three until leaf nodes are conirmed. 

5. Build the Random forest model by reiterating the steps one to four for ‘n’ times to build ‘n’ number of trees. 

18. Explain in brief about Neural Network Fundamentals. 

A Neural network is an artificial representation of the human brain that attempts to simulate its learning process. The neural network understands the patterns from the data and utilizes the the information that it acquires to predict the output of new data, with no human assistance. 

19. Explain about auto-encoders.

Auto-encoders are called as learning networks. They ensure minimum possible errors while transforming inputs into outputs. Therefore, Auto-encoders tries to confirm if required output is equal or as close as to input. 

20. Explain root cause analysis? 

Root cause analysis initially designed with  motive to analyse industrial accidents. It is basically a problem-solving method utilized for isolating the root causes of problems or faults. 

Read: Mandatory Skills to Become Data Scientist

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Responsibilities of a Data Engineer?

Data Engineers are responsible for organizing the data in a structured and easy to get to format for the organization they work for. Data Analysts and Data Scientists use this data to come up with insights, which inform the business or develop data products that improve the customer journey.

• Create, construct, install, test and maintain data management systems. They create managerial databases or data lakes to store all pertinent data points.
• Confirm that systems meet industry practices and business needs.
• Examine prospects for data procurement and the latest events for existing data.
• Slot in new data management technologies and software engineering tools into existing actions.
• Work with modellers, data architects, and IT team members to achieve project goals.

What are the skills required by a Data Engineer?

1. Technical skills 

2. Effective Collaboration 

3. Intellectual Curiosity 

4. Industry Knowledge 

What are the responsibilities of a Data Analyst?

The roles and responsibilities of a Data Analysts include analysing data and deriving insights from it. They use statistics, investigative Data Analysis and Machine Learning to assess the data at hand. They help organizations to understand:

• Marketing and sales predictions.
• Resource optimisation
• Attrition of employees in an organisation.

What are the job requirements and skills of a Data Analyst?

• Business Domain Knowledge – Data Analysts are business problem solvers. Therefore, they need to have an sharp understanding of the business, in order to clearly define the problem and come up with quantitative solutions.

• Analytical and Statistical Skills – Data Analysts operate with large quantity of data, figures, facts and crunch numbers. They need to know statistical and machine learning techniques to analyse the data in order to reach conclusions and be able to make recommendations.  

•  Technical Skills: Data Analysts sieve through huge amounts of data. They need to know specialised languages like R and Python to perform analysis and be familiar with SQL to manage data and derive quick trends.

• Communication Skills – Data Analysts are often required to present findings or decode the data into an understandable document. They must communicate complex ideas in the best way possible.

Where do Data Scientists healthy in?

A Data Scientist embodies the perfect combination of business knowledge, technical know-how and figures. As a Data Scientist your job is not to simply sketch insights and trends from the data collected over a period of time, but to also create intelligent systems which companies can organize to automate decision making.

Requisite skills for a data scientist?

• Knowledge of algorithms, statistics, mathematics and machine learning.
• Programming languages such as R, Python, SQL, SAS, and Hive.
• Business understanding and the aptitude to frame the right questions to ask, and find answers in the available data.
• Communication skills in order communicate the results effectively to the rest of the team.

However, the day-to-day job of a Data Scientist varies a lot.

Read: Mandatory Skills to Become Data Scientist

Difference between Data Scientist and Data Analyst

To explain in more detail, they both deal with data, but the key difference between Data Scientist and Data analyst is what they use the data for? Data Scientist roles and responsibilities include interpret data. They also have to code and prepare models for the better future of the company. A Data Scientist already has a higher degree which in some cases, they might have already performed the roles and responsibilities of a Data Analyst. If it still confuses you that exactly what does a Data Scientist do, then understand the fact that they are skilled and higher in programming and the current processes of data modelling. But they also possess the skills of Data Analyst.

All we can say about what the role of Data Analyst is is juggling between data and identifying new trends. To make decisions based on the insight and to create graphical representations, and to showcase to the company what the data reveals is exactly what the work of a Data Analyst is.

Conclusion

The Data Engineer manages the data needs of the organization. Data Analysts provide insights. Data Scientists create data products which can make the user experience faultless. It is important to keep in your mind that these definitions and roles may vary in different organizations.

Also, read: 7 Factors Companies Look for When Hiring a Data Scientist

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Data science covers a variety of topics that includes deep learning, internet of things and artificial intelligence, etc. we can say that data science is not a single term or it can solve the variety of business problems that includes algorithm computation, analysis, a mix of data inference, and technology.

It also provides companies with advanced tools and technologies to help them automate complex business processes related to extracting, analyzing, and presenting raw data. Since there is so much going on in the technical field and data is generated quickly, it is important to know the latest and upcoming trends in data science.

To keep you up to date on data science trends, we have compiled a list of the five most important data science trends that should help your business succeed.

Read More: What is Artificial Intelligence Course Fee in Bangalore?

1. Access to Artificial Intelligence.

Artificial intelligence has become the common technology for small and large scale business and it will thrive in the next few years. We are in the early stages of using artificial intelligence technology but in 2020 we will see more advanced uses of this technology. What is the reason that AI is growing fast? The reason is companies can improve all of their business processes and customer data by using AI.

Although the use of AI remains a challenge for many, it is not so easy to research the evolution of this technology. In 2020, we will find more advanced applications developed with AI, machine learning and other technologies that can improve the way we work. Another trend that is conquering the market is automated machine learning, which will help transform data science through better data management. Therefore, you may need special training to perform deep learning.

2. Rapid Growth of IoT

According to an IDC report, investment in IoT technology is expected to reach $ 1 trillion by the end of 2020, signaling the growth of smart and connected devices. Even in 2019, we used apps and devices that we can use to control our household appliances like air conditioning, TV, etc. Most of you may no longer know this, as this is only possible via the Internet of Things.

IoT is continuously attracting user’s attention. If you have ever encountered smart devices like Google Assistant and Microsoft Cortana that we can use to automate normal things, then you will get to know the power of IoT. This will encourage the companies to invest in this technology, especially in the development of smartphones where IoT is most commonly used.

3. Rapid Change in Big Data Analytics

When we talk about data science, we cannot ignore big data. Most of the companies are using big data to achieve their business goals. Many enterprises are using different kinds of tools to analyze data; python is mostly used in this.

Businesses are also focusing on the predictive analysis that helps them to create smarter strategies for their businesses. Predictive analysis helps you to identify the interest of your customers based on their browsing history and their purchase. Depending on that, you can able to create strategies for your businesses.

4. Edge Computing is on Rising

Edge calculation is currently driven by sensors. With the growth of the IoT, however, edge computing will take over traditional cloud systems. Edge computing enables businesses to store streaming data near data sources so that they can be analyzed in real-time. It also provides an excellent alternative to big data analytics that require high-end storage devices and higher network bandwidth.

As the number of devices and sensors for data collection grows rapidly, companies are turning to advanced computing because it can solve bandwidth, latency, and connectivity issues. Combined with cloud technology, edge computing can provide a synchronized structure that helps minimize the risks associated with data analysis and management.

5. Data Science Security Professionals

The introduction of artificial intelligence and machine learning will create many new roles in the industry. Data science security professionals will be in high demand. Since artificial intelligence and machine learning are completely dependent on data and are supposed to process this data effectively, data scientists must have specialist knowledge of data science as well as computer skills.

Although the corporate market already has access to much knowledgeable data science and IT professionals, there is still a need for more professional data security professionals who can safely process customer data. For this purpose, data security scientists must be familiar with the latest technologies in data science or big data analysis. For example, Python is one of the most commonly used languages ​​in data science and data analysis. So, if you know the concepts of Python well, you can solve problems related to the security of data science.

Conclusion:

Data science has become the popular technology of 2020. Here I have shown you the top 5 data science trends in 2020. You can check these trends and based on this you can analyze your business and get to know where you have to improve. I hope you have understood the popularity of data science.

Near Learn is the Best Data Science Training institute in Bangalore and provides training on Artificial Intelligence, Machine Learning, Deep Learning, Full-Stack Development, Mean-Stack development, Golang,  React Native and other technologies as well.

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