Data Science
Become a Data Scientist in 2024 with These Following Steps
Fundamental points required to be on a path of data scientist
Data scientists are now everybody’s dream job/work. First, do a question to yourself, Am I want to become a data scientist? When you feel to learn new things from your inner gut then start to take a learning path. The transition from other fields to the data science field is very difficult because it requires learning new tools and languages. But, don’t worry, I will make this skill requirement journey a little bit easier for you. This article will give you an overview of topics to learn to become a path of a data scientist.
Responsibilities :
- Collection of data and Cleaning — that is pre-processing
- Doing statistical analysis on the data
- Visualizing and making inference from the data
- Modelling good algorithms for future prediction
Suppose you are very new and know a little about this field and ready to take challenges to learn.
Step 1: Learn the basics of the data science field and its application.
- First we need data to be working on. From where these data come from this is the basic need. The data can come from everywhere in real life.
- Almost most of the time we are solving a problem to improve the business revenue and to help the society.
- In the business industry, we have food chain, real state, finance sector, consumer goods.
- For society we need to develop new products to help them. Like, rain forecasting, vaccine development, etc.
- Working with data that need to be collected from their particular field.
- The data need to be stored in a readable digital format.
- The data can be used to make reports and future predictions for business by using advanced statistical tools that are machine learning algorithms.
Step 2: Learn the backbone of data science i.e. Statistics.
- The statistics are the basic need we need to develop for the data science approach.
- The statistics need the type of data we are working with. It can be population data or sample data. Most of the time we work on sample data only.
Statistics can be descriptive and inferential.
Descriptive Statistics
This part deals with the organizing and summary of data in terms of central tendency measurement and spread of the data. The topics come in this analysis are shown below:
- Types of data: It is a category or numerical data.
- Graphs and plots to check the relationship between two types of data.
- Skewness: It tells the shape of the data and where most of the data lie.
- Spread of data : It deals with variance and Standard Deviation.
Inferential Statistics
This part deals with the conclusion and prediction of the population by analysis of sample data. The topics come in this analysis as shown below:
- Distributions: The data can be analyzes with many distribution and standard errors.
- Confidence intervals: It deals with a range of the most information lies.
- Hypothesis Statistics: It deals with the null hypothesis and alternate hypothesis to be true or not.
Step 3: Choose a programming language to build models.
Nowadays there are many tools with which we can do statistics and predictive modeling.
Open Source:
- Python: It is used to do programming in various fields and mostly in statistics and machine learning approach in data science.
- R: It is used for statistical and graphical analysis.
Commercial Source:
- SPSS: It is also used for statistical analysis a tool from IBM.
- SAS: It is used for business, predictive, data management analysis, etc.
Step 4: Learn basic math of algebra and calculus.
Math is very important for a data analysis person for choosing a good algorithm for suitable problems.
- Algebra: It is a study of vectors, matrices, and functions, etc.
- Calculus: It is a study of integration, differentiation, limits, etc.
- Well not only these two areas but many topics need to learn and revise to understand the working of algorithms and formulae.
Step 5: Learn Machine Learning algorithms and their working.
This is the most buzz word in the field of data science. All the predictions come after modeling data with machine learning algorithms. There are many concepts in this area to learn about choosing a good algorithm. Machine learning algorithms are divided into three categories as shown below:
- Supervised Learning
Supervised Learning is those algorithms on data that have known target or dependent variable. The target variable can be in numerical and category.
Numerical data used for regression algorithms like Linear regression for linear, Logistic regression for non-linear, SVM and random forest can use for both regression and classification.
Category data used for classification algorithms like the random forest, KNN, Decision tree in which we classify objects of different types.
- Unsupervised Learning
Unsupervised learning is used for clustering in this the data don’t have dependent or target value. The algorithm comes in this type are K-means, Hierarchical, DBScan clustering. These modelings give an analysis of the comparison between clusters.
- Reinforcement learning
This type of learning is based on learning from its error. The learning is based on minimizing the error after every training and modeling which takes automated decisions.
Conclusion:
The data science aspiring people need at least a basic idea of what we learn in this field. These steps may not be whole topics covered but a basic idea covered.
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