(AGENPARL) – THE HAGUE (THE NETHERLANDS), mer 29 luglio 2020

Data science has grown by leaps and bounds in the past few years. According to Research & Markets, nearly 90% of business professionals say data and analytics will be a key part of their digital transformation, so it’s not surprising that there is a constant demand for data science professionals across the industry.

Not to mention, the good paycheck, promising growth, and consistently challenges at work make **data science career** extremely fulfilling. However, what’s challenging, is becoming a data scientist.

A data scientist needs an extensive set of skills. Below are the skills that you need to get started in data science.

- Mathematics
- Statistics
- Programming
- Machine Learning and

Advanced Machine Learning (Deep Learning) - Data Visualization
- Big Data
- Data Ingestion
- Data Munging
- Tool Box
- Data-Driven Problem

Solving

Let’s delve into each skill one by one.

**1. Mathematics**

A common assumption among aspirants is if you’re not good at maths, you will not be a good data scientist. Mathematics is a crucial part of data science, but it’s not the *only *skill to master. Strong skills mathematics builds a strong foundation for data science. The major concepts you need to learn are:

- Matrices and Linear Algebra Functions
- Hash Functions and Binary Tree
- Relational Algebra, Database Basics
- ETL(Extract Transform Load )
- Reporting VS BI (Business Intelligence) VS Analytics

**2. Statistics**

Statistics form the backbone of data science. All data analysis, predictive models, and forecasting models that are frequently used in data science are built on statistical concepts. It further helps data scientists to know more about data, which further helps in finding the right techniques to solve problems. There are two major branches of statistics that data scientists are expected to master: Descriptive and inferential statistics.

**Descriptive statistics**: Introduces you to concepts such as measures of central tendency (mean, median, and mode), measures of dispersion (covariance, standard deviation, variance), etc. that help data scientists understand raw data better.

**Inferential statistics:** Deriving insights from data. It introduces data science professionals to concepts such as regression, probability mass functions, cumulative distribution function, coefficient of correlation, etc. This helps relationship among various data and derive insights from it.

Both descriptive and inferential statistics are important to excel as a data scientist. This is also why – statistics form the most significant part and large part of globally recognized data **science certifications and courses. **

**3. Programming**

Programming is what separates data scientists from statisticians. Not that it is the only skill that separates the two, but programming is amongst the few differences. Programming executes repetitive processes faster and speeds up to several manual processes and long tedious including data collection, exploratory data analysis, etc. Processes that earlier took 2-3 hours to take 1 minute or less. R and Python are two most widely used programming languages in data science.

**Pick one language and master it**.

**4. Machine learning**

This is the last but most impactful and significant part of the data scientists’ job. Machine learning is used to build models – prediction, forecasting, etc. Models automate manual tasks. Companies constantly try to build models that can automate their cognitively demanding tasks. To excel at building machine learning models, you will need to know the following concepts.

- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- K Nearest Neighbor
- Clustering (for example

K-means)

**5. Deep learning**

Deep learning overcomes the challenges of traditional machine learning approaches. Though initially knowledge of deep learning isn’t sought by companies, knowledge of deep learning accelerates the pace of **data science caree**r growth. You should cover the following topics as part of deep learning:

- Fundamentals of Neural

Networks - Anyone library used for

creating Deep Learning models, such as Tensorflow or Keras. - Understand how

Convolutional Neural Networks, Recurrent Neural Networks and RBM and

Autoencoders work

**6. Data visualization**

Before building a model, data scientists are expected to present their findings in a visible and easy to understand graphs and plots. To learn data visualization, you will need to master data visualization skills.

- Tableau
- Kibana
- Google Charts
- Datawrapper

So, what are you waiting for? Get your notebook and pen/pencil and start learning data science.

##### *Image Credit: https://dv-website.s3.amazonaws.com/uploads/2019/10/mk_datascientist_2019.jpg*

Fonte/Source: https://datafloq.com/read/6-skills-become-master-data-scientist/8859