Want to be a Data Analyst ? Follow this Path | Data Analyst Roadmap

 Data Analyst is one of the booming career and this career is attracting most of the people but due to not having the right knowledge, they move to the wrong track. So, if you want to move on the right track then follow the below points, so that you become an good Data Analyst.

The steps are well defined along with the topics that you should learn to become a good data analyst.

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The complete journey of becoming an Data Analyst is divided into the following steps. 

1. Programming

Python and R are the most famous and most used in the field of Data Science. Most of the beginner's starts their journey with the python. Python is very easy language and best language to start the journey of Data Analyst.

The concepts that you should learn in Python :- 

1. Basic of Python
   
2. List, Tuples, set, Dictionary
   
3. Functions, Object Oriented Programming
   
4. Libraries :- NumPy, Pandas, Matplotlib, Seaborn
   

R is used for statistical computing and most famous language after python in the field of Data Science. R was released around 28 years ago. If you are from  a statistics background then you will find R easier.

The concepts that you should learn in R :- 

1. Basic of R
2. Vector, List, Data Frame, Array, Matrix Function
3. Libraries :- shiny, tidyr, dplyr

2. Data Structures & Algos

It is very important that Data Scientist/Data Analyst should have knowledge of Data Structure and various algorithms. Data Structure means to organize the data in such a way in a system that when we need that it is easy to access and use.

To structure data is important as we all know data is increasing day by day and it will keep on increasing. So, to handle such amount of data is a challenge that's why data structure is very important.

Algorithms are the defined steps that we follow in a step by step manner to complete a specific task. you all have listened about the YouTube algorithm, Social Media algorithms, Digital Marketing algorithms so, these industry is working on the algorithms.

The concepts that you should know :- 

• Array
• String
• Linked List
• Dynamic Programming
• Searching
• Sorting

3. Mathematics

Mathematics is the core of the Data Science. Mathematics plays a fundamental role in Data Science. If you will be good in mathematics then you can easily understand the algorithms and what is happening in the algorithm and why. you will be aware about the each line of code that why it is written. If you want to go in research then you should have solid grip in mathematics.

There are some of the topics that if you will learn, then your math's will be good.

The topics are :- 

1. Linear Algebra
2. Analytics Geometry
3. Matrix
4. Vector Calculus
5. Regression
6. Density Estimation
7. Classification 

4. Probability

Probability is one of the most important concept in Data Science. Probability is not very complicated, it tells about the event that how much chance is there that a particular event will occur.

 In probability there are many concepts but the concepts that you should learn and that will help you in your journey are :- 

1.  Random Variable
2. Joint Probability Distribution
3. Discrete Distribution (Binomial, Bernoulli, Geometric)
4. Continuous Distribution (Uniform, Exponential, Gamma)
5. Normal Distribution

5. Statistics

Statistics is the grammar of science. Karl Pearson

Statistics is the backbone of the Data Science. Statistics concepts helps you to make better business decisions from data. Knowing the concepts of statistics helps you to think critically. Most of the time of Data Scientist or Data Analyst spends in pre - processing of data and it requires a good knowledge of statistics.

The concepts that you should know in statistics are :- 

• Random Samples
• Sampling Distribution
• Parameter Estimation
• Hypotheses Testing
• Stochastic Process
• Reliability Engineering
• Computer Simulation
• Design of Experiments
• Correlation
• Simple Linear Regression
• Multiple Regression
• Statistical Quality Control
• Basic Graphs
• Nonparametric Statistics

6. Machine Learning

In machine learning we trains a machine on a particular data, so that when new data came it can predict the about the data. In machine learning we build models that predict about the data and it learns gradually and increases it accuracy by it's time. if you want to go in research then you should know the mathematics behind each and every line of code of an algorithm. If you don't want to go in the research then you don't need to go in the deep.

The concepts that you should learn :- 

• Supervised 
• Unsupervised
• Data Exploration
• ML Models
• Model Validation
• Underfitting & Overfitting
• Handling Missing Values
• Handling Categorial Variables
• Data Leakage

7. Dashboarding

Here we present our insights that we have drawn form the data. Insights are present in the form of charts and graphs. There are tools that are used for the vitalization purposes. Communication skills plays an Important role here. When you will present your insights to stakeholders, they don't know about data. so, you have tell them in a simple language.

The tools that are used for visualization are :-  

1. Tableau
2. PowerBI
3. Qlik Sense

Conclusion

The important concepts that were necessary has been discussed above.

The Data Science Steps are :- 

1. Data Collection
2. Data Cleaning
3. EDA
4. Model Building
5. Data Visualization
6. Deployment

You can read about that more in detail. Now, you start your data science journey today.