You are here because you have a keen interest in Data Science. Well, if you want to set yourself on the journey of learning of data science, there are a few requisites for it. However, mathematics and programming stand above all of them. You need not be an expert,but need to have the basic knowledge to not feel like a fish out of water when you step into learning data science.
Here’s everything you need to cover in Mathematics before you begin learning Data Science.
While it comes to programming for data science, Python is the language is used and is definitely, the easiest.
Working with Python
Python is an object-oriented approach and comes with practice in data processing, analysis, and visualization. It is a popular language and easier to grasp as a few lines of code can get the job done. The few widely used libraries with data science are,
NumPy: Almost all numerical calculations in Python are mostly centered around the NumPy package. This package offers users the functionality to create multi-dimensional array objects for the required calculations in Python.
SciPy: SciPy is an extension of the NumPy package and is built on it. This package holds a collection of mathematical algorithms and sophisticated functions that can be used in your programming. SciPy package serves a lot of utilities that are not provided by other libraries like vector quantization, statistical functions, n-dimensional image operations, integration routines, interpolation tools, sparse linear algebra, linear solvers, optimization tools, signal-processing tools, sparse matrices, and more.
MatPlotLib: Like SciPy is built on NumPy, MatPlotLib is built both on SciPy and NumPy. This library is useful to create visual representations of your dataset or data analysis findings.
Working with R
R is an open-source, free statistical software that is widely used across the data science projects. It can be slightly complicated as compared to Python. This source, however, has the power to handle advanced statistical analyses and also, built with advanced data visualization capabilities. The widely used. The following R packages are widely used in data science.
Forecast: The main idea behind using this package is forecasting functions that can adapt to the use of ARIMA (Auto Regressive Integrated Moving Average) or other univariate time series forecasts.
Mlogit: Mlogit is the abbreviation for a multinomial logit model, in which the observations of one class used to train software that can carry out observations of whose classes are not known. This is the aptest package for logistic regression in R.
ggplot2: This package in R is the fundamental data visualization package in R. With this package you can create data graphics like histograms, scatterplots, bar charts, box plots, and density plots. Not only this, but you also get a wide range of design options like colors, layout, transparency, and line density.
Also Read: An Overview Of Data Science As Career: FAQs
Working with SQL Queries in Data Science:
SQL or Structured Query Language is a set of rules that help you perform database operations like query, modify, add, or remove data. Knowledge of SQL query helps you handle queries and data manipulation.
A proper understanding of Data query language and data manipulation language will help you perform operations like select, insert, alter, delete, and update based on conditions (or not, when you want to retrieve it all). You can also aggregate data by using groups and order the data using orders. The use of SQL helps you get properly filtered data.
Limiting the coding
At the beginning of the article, it is said that you need to have some knowledge of coding. Well, you need to, but don’t have to be an expert. There are certain applications that will help you complete your project without being an expert in coding. You can use the following, for instance.
Microsoft Excel: This is the king of data handling. You must a little acquainted with it and growing your knowledge in Excel will help you a lot with data management with data science. You can automate a lot of things to do your work.
KNIME: Konstanz Information Miner (or KNIME), is a free and open-source data analytics, reporting, and integration platform. This can be used by beginners for a code-free predictive analysis. It also offers plugins that can be used by various advanced users. This comes to help in doing upsell and cross-sell, customer churn reduction, sentiment analysis, and social network analysis.
These are certain things you need to know as a data science learner. You go step by step and learn data science. Having the required knowledge while moving ahead, will make you a great Data Scientist. Good luck!