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

CondaValueError: Value error: invalid package specification

Recently I was trying to create Conda Environment and wanted to install Tensorflow but i have faced some issue , so i have done some research and done trouble shooting related to that . Here am going to share how to trouble shoot if you are getting Conda Value error while creating Conda environment and install tensorflow . Open Anaconda Prompt (as administrator if it was installed for all users) Run  conda update conda Run the installer again Make sure all pkg are updated: Launch the console from Anaconda Navigator and conda create -n mypython python=3.6.8 After Installing Conda environment please active the conda now :  conda activate mypython once conda environment has been activated kindly install tensorflow 2.0 by using this command pip install tensorflow==2.0.0 once Tensorflow has been successfully install kindly run the command :  pip show tensorflow Try to Run Comman PIP Install Jupyter lab and after ins...

Important Python Libraries for Data Science

Python is the most widely used programming language today. When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. Python is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance language, and there are many more benefits to Python programming.People in Data Science definitely know about the Python libraries that can be used in Data Science but when asked in an interview to name them or state its function, we often fumble up or probably not remember more than 5 libraries. Important Python Libraries for Data Science: Pandas NumPy SciPy Matplotlib TensorFlow Seaborn Scikit Learn Keras 1. Pandas Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. With around 17,00 comments on GitH...

How to deal with missing values in data cleaning

The data you inherit for analysis will come from multiple sources and would have been pulled adhoc. So this data will not be immediately ready for you to run any kind of model on. One of the most common issues you will have to deal with is missing values in the dataset. There are many reasons why values might be missing - intentional, user did not fill up, online forms broken, accidentally deleted, legacy issues etc.  Either way you will need to fix this problem. There are 3 ways to do this - either you will ignore the missing values, delete the missing value rows or fill the missing values with an approximation. Its easiest to just drop the missing observations but you need to very careful before you do that, because the absence of a value might actually be conveying some information about the data pattern. If you decide to drop missing values : df_no_missing = df.dropna() will drop any rows with any value missing. Even if some values are available in a row it will still get dropp...