Skip to main content

Daily Task performed by Data Scientist at Work place - Life of a Data Scientist

Data Science is a multidimensional field that uses scientific methods, tools, and algorithms to extract knowledge and insights from structured and unstructured data.But in reality, he does so much more than just studying the data. I agree that all his work is related to data but it involves a number of other processes based on data.Data Science is a multidisciplinary field. It involves the systematic blend of scientific and statistical methods, processes, algorithm development and technologies to extract meaningful information from data.

The average Data Scientist’s work week as follows:

Typical work weeks devour around 50 hours.
The Data Scientists generally maintain internal records of daily results.
The Data Scientists also keep extensive notes on their modeling projects for repeatable processes.
The good Data Scientists can begin their career with a $80k salary, and the high-end experts can hope to make $400K.
The industry attrition rate for DS is high as organizations frequently lack a plan or visions for utilizing these professionals.

"Data Scientists was that when an algorithm actually solves a real-world business problem, the feeling of pride and satisfaction that comes with it is the greatest reward for the professional."



Working With Data, Data Everywhere

A data scientist’s daily tasks revolve around data, which is no surprise given the job title. Data scientists spend much of their time gathering data, looking at data, shaping data, but in many different ways and for many different reasons. Data-related tasks that a data scientist might tackle include:

Pulling data
Merging data
Analyzing data
Looking for patterns or trends
Using a wide variety of tools, including R, Tableau, Python, Matlab, Hive, Impala, PySpark, Excel, Hadoop, SQL and/or SAS
Developing and testing new algorithms
Trying to simplify data problems
Developing predictive models
Building data visualizations
Writing up results to share with others
Pulling together proofs of concepts
All these tasks are secondary to a data scientist’s real role, however: Data scientists are primarily problem solvers. Working with this data also means understanding the goal. Data scientists must also seek to determine the questions that need answers, and then come up with different approaches to try and solve the problem.

Now we have understood the process of data science. This was a look at a day in data scientist job and his tasks. Specific tasks include:

  • Identifying the analytical problems related to data that offer great opportunities to an organization.
  • Collecting large sets of structured and unstructured data from all different kinds of sources.
  • Determining the correct data sets and variables.
  • Cleaning and eliminating errors from the data to ensure accuracy and completeness.
  • Coming up with and applying models, algorithms, and techniques to mine the stores of big data.
  • Analyzing the data to uncover hidden patterns and trends.
  • Interpreting the data to discover solutions and opportunities and making decisions based on it.
  • Communicating findings to managers and other people using visualization and other means.

Labels:

Comments

Post a Comment

Popular posts from this blog

Statistics Interview Questions Part-1

Q1. What is the difference between “long” and “wide” format data? In the  wide-format , a subject’s repeated responses will be in a single row, and each response is in a separate column. In the  long-format , each row is a one-time point per subject. You can recognize data in wide format by the fact that columns generally represent groups. Q2. What do you understand by the term Normal Distribution? Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell-shaped curve. Figure:   Normal distribution in a bell curve The random variables are distributed in the form of a symmetrical, bell-shaped curve. Properties of Normal Distribution are as follows; Unimodal -one mode Symmetrical -left and right halves are mirror image...
Post a Comment
Read more

Scope of an Artificial Intelligence

Artificial Intelligence has grown exponentially in the past decade, and so have the career opportunities as an AI expert/specialist. But what exactly does an AI expert do? Also, is becoming an expert the only option while pursuing a career in artificial intelligence?I don’t have any programming/ coding background. Can I still work as an AI expert? And, what specialization or skill set do I need to acquire to get into this field? Skills Required to Build a Career in Artificial Intelligence 1. Sound Mathematical and Algorithmic Understanding To be an ideal candidate in AI, you need to have solid knowledge of applied mathematics and a set of algorithms. Having proficiency in problem-solving and analytical abilities will help you in performing tasks in a more efficient way. You must also have reasonable knowledge of statistics and probability. This helps in understanding various models of AI, like Naive Bayes, Gaussian Mixture Model, etc. 2. Basic Know-How of Programmin...
Post a Comment
Read more

Data Science Interview Questions -Part 2

1) What are the differences between supervised and unsupervised learning? Supervised Learning Unsupervised Learning Uses known and labeled data as input Supervised learning has a feedback mechanism  Most commonly used supervised learning algorithms are decision trees, logistic regression, and support vector machine Uses unlabeled data as input Unsupervised learning has no feedback mechanism  Most commonly used unsupervised learning algorithms are k-means clustering, hierarchical clustering, and apriori algorithm 2) How is logistic regression done? Logistic regression measures the relationship between the dependent variable (our label of what we want to predict) and one or more independent variables (our features) by estimating probability using its underlying logistic function (sigmoid). The image shown below depicts how logistic regression works: The formula and graph for the sigmoid function is as shown: 3) Explain the steps in making a deci...
Post a Comment
Read more