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.

Comments

Popular posts from this blog

Introduction to Datascience

Data Science has become one of the most demanded jobs of the 21st century. What is Data Science? “Data Science is about extraction, preparation, analysis, visualization, and maintenance of information. It is a cross-disciplinary field which uses scientific methods and processes to draw insights from data. ” As a data scientist, you take a complex business problem, compile research from it, creating it into data, then use that data to solve the problem. A Data Scientist, specializing in Data Science, not only analyzes the data but also uses machine learning algorithms to predict future occurrences of an event. Therefore, we can understand Data Science as a field that deals with data processing, analysis, and extraction of insights from the data using various statistical methods and computer algorithms. It is a multidisciplinary field that combines mathematics, statistics, and computer science. Why Data Science? So, after knowing what exactly Data Science is, you must explore ...

15 Common questions for Machine Learning...!!

  1. What is logistic regression? Logistic regression is a machine learning algorithm for classification. In this algorithm, the probabilities describing the possible outcomes of a single trial are modelled using a logistic function. 2. What is the syntax for logistic regression? Library: sklearn.linear_model.LogisticRegression Define model: lr = LogisticRegression() Fit model: model = lr.fit(x, y) Predictions: pred = model.predict_proba(test) 3. How do you split the data in train / test? Library: sklearn.model_selection.train_test_split Syntax: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42) 4. What is decision tree? Given a data of attributes together with its classes, a decision tree produces a sequence of rules that can be used to classify the data. 5. What is the syntax for decision tree classifier? Library: sklearn.tree.DecisionTreeClassifier Define model: dtc = DecisionTreeClassifier() Fit model: model = dtc.fit(x, y) Predictions: p...

What is P Value ?

In Data Science interviews, one of the frequently asked questions is ‘What is P-Value?”. According to American Statistical Association, “A p-value is the probability under a specified statistical model that a statistical summary of the data (e.g., the sample mean difference between two compared groups) would be equal to or more extreme than its observed value.”  That’s hard to grasp, yes? Alright, lets understand what really is p value in small meaningful pieces to make it very clear. When and how is p-value used? To understand p-value, you need to understand some background and context behind it. So, let’s start with the basics. p-values are often reported whenever you perform a statistical significance test (like t-test, chi-square test etc). These tests typically return a computed test statistic and the associated p-value. This reported value is used to establish the statistical significance of the relationships being tested. So, whenever you see a p-valu...