Data Science Career Path
Data Science Career path – Data Science is a field moving around the Data where professionals are working with different types of Datasets and analyzing these data sets to find future forecasts and building predictive models. Let me guide you about this field so here I am going to explore how you can make a career in Data Science and what you need to do before understanding this subject.
Firstly, Forget about Science just hold “Data”, now think about where is data and what is the impact of data in any organization? how the company manages its huge volume of data.? Let me give you an example- Let us say you are going to purchase a shirt from Amazon then Amazon stores your purchase order in database management systems like Oracle, MySQL, Postgres SQL, etc.
Now Think millions of customers are placing orders and all the Data going inside RDBMS(Oracle) and the company wants to see all sales on monthly basis with good representation like in the form of a graph moreover company also wants to see their future sale forecast.
This is the main point where the Data Science field came into existence after data loading and cleaning now it’s time to build predictive models using the python programming language so first we train our model using training Data set and then we test our model using test data set. confused, let me explain to you by an example. ley us you are giving training to your pets so first, you will show them how to do a task(Training) after some time you test that your pet is working the task perfectly or not(Testing).
Data Science Field is growing day by day but Why? very simple data is growing day by day and we need professionals who can process and analyze these huge volumes of data using different tools and technologies.
Prerequisite to learn Data Science:
1- Basics Math and Basic Knowledge of computers.
2- Basics of any programming language.
3- Basics of the database.
4- Basics of Stats
Let us see the step-by-step procedure to learn Data Science.
step-1- start learning python programming basics like variables, loop if-else statements, and functions.
step-2 – Next part is Numpy and Pandas library of python.
step-3- Start with Basic statistics like mean, median, mode, quartiles, boxplot, correlation, and hypothesis testing, etc.
Step-4- Next part is Machine learning predictive Models -Linear Regression, classification, SVM and Ensemble Learning, etc.
Step-5- Start Tableau and Matplotlib for Data Visualization.
Step-6- Next part is Neural Network using TensorFlow and Keras.
Thanks, Hope this will help.