For a beginner, 3 months is a very short time to understand data science. In such a short time, you may only be able to grasp what it is and the way industries apply it to get data-driven insights. If you are to understand data science as a beginner, you may need about 6-8 months at least.

There are some modules that can help you learn data science. There is also an estimate of the time duration needed to at least grasp the concepts before getting started. Some of the important modules include:

**Maths**:

maths can be considered a prerequisite. We learn math in colleges and schools in the normal course of learning. Engineering students need to be familiar with maths as well. The main foundation is calculus, statistics, probability, and linear algebra. Anyone without a grip on the topics may take a month to learn. These are a part of machine learning. Machine learning is the foundation of data science. This is to say, you cannot learn without grasping these topics. Maths is crucial for a data analyst because he or she has to handle number values among other data sets.

**Programming knowledge**:

there are different programming languages that can be used for data science implementation. These include R, Python, Java, and C++. Python is the most popular and widely used language compared to others. This is because it is rather easy to understand and learn. It also happens to be free. Normally, an experienced person can take one month to learn Python intended for use in data science. A beginner, on the other hand, may need two months.

**Machine learning:**

you need to invest enough time in machine learning. This includes unsupervised and supervised learning. It also involves reinforcement learning. For this, you should dedicate one and a half months to machine learning. Another month should be dedicated to case studies and practice projects. There are some resources available where you can practice.

**Deep learning:**

this is a machine learning subset. It is one of the main AI applications. DL involves the use of a large data set and learning through neural network architecture. This is quite interesting, and you may need 20 days to one month to learn.

**Natural language processing**:

it is abbreviated as NLP. This blends artificial intelligence, human language, and computer science. It has functions such as distance algorithms, text reprocessing, text analysis, Dirchlett distribution, topic modeling, and an information retrieval system. To fully understand natural language processing, you need about 2 weeks.

Data science is getting popular daily, and different technologies are being applied to different algorithms and methods to get value from data. Techniques such as science, statistics, and maths are needed for one to thrive in this field.

For one to learn data science in just three months, then you may need to already have learned the basics. Anyone with a foundation in statistics, science, and mathematics can learn faster and more efficiently compared to a beginner.

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**How can it be achieved?**

You need to be passionate and dedicated to learning data science. If you want to pull it off in 3 short months, there are some capabilities you must already bear. In this way, you will have made significant progress in three months. They include the following:

- Have some level of technical knowledge, such as a degree in math, statistics, and so on
- You should know programming languages and coding schemes
- It is an advantage if you have some experience working in a data science environment

Data scientists use statistics and math strategies to analyze data. A data scientist is important in all organizations. You can manage to be a data scientist in three months if you already have the basics. There are more guidelines that can make it easier to achieve. They include:

**Start with data analysis**

To elevate to the rank of a data scientist, you need to be a data analyst first. When you have strong data analysis basics, data science becomes much easier to handle. There are industries where data science degrees are offered. You should grasp data analysis concepts and grasp data science concepts like

- Mathematics and statistics
- Python programming language
- HIVE and PIG
- Data gathering
- MS Excel
- Data visualization

**Advanced skills in ML or machine learning**

Once you master data analysis in a month, it is time for machine learning. Artificial intelligence is needed in machine learning. There are skills that you should have to learn including:

- Computer fundamentals
- Programming skills
- Statistics and probability
- Data calculation skills

These are skills that you can learn from tutorials and online lectures. You can complete this in a month.

**Data science tools**

When starting the third month, you will have already covered machine learning and data analysis. As such, you are capable of handling environments that are really related to data science. There are some common tools that need to be mastered in the third month, including:

- Tableau: it is needed for data visualizations. It can be used to design graphs and charts for easier presentations
- SAS: this is a programming language tool designed for statistical operations
- Gplot2: this is a tool designed for the programming language R. It offers some advanced capacities for visualization of data.

**Bottom line**

Data science aims at getting valuable information to help in the decision-making process in an organization. It is needed for strategic planning as well. It uses scientific concepts and the best analytic tools for the most accurate outcome. With data science, organizations are aided in better sales and marketing campaigns. This in turn boosts the effectiveness of their operations.

Data science is important to all industries making it marketable. Learning can be a good idea if you want to start or elevate your career further. The duration of time it takes to complete a data science course is not uniform for everyone. Someone with experience and basic knowledge in data science take less time compared to someone who is just beginning.

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