Many people have this question in mind while entering into Data related fields. These two are different job profiles but the destination or career path is one and the same.

Both are good career opportunities but you need to decide between the two based on your goal on what you want to become. Here we have discussed both the roles thoroughly.

 

Data Scientist vs Data Analyst

Data Analyst is the entry level job whereas Data Scientist is a much advanced career or job role. People while moving into data related fields, first go with Data analyst related job roles. They practice fully fledged into this role and once they have experience of around 5-7 years, they move into the role of Data Scientist. The kind of work differs between Data Scientist and Data Analyst. Data Scientist is much more about analysis of data and performing visualization of data on tools like Tableau or POWER BI.

It is more of running SQL queries or python codes for analysis purposes. Data Scientist is more of an advanced career path. It involves algorithms and AI or ML based algorithms to implement using coding. Data scientist is an advanced role and requires some years of experience before moving into it.

A data scientist is required to create pipelines for data flow, analyse it, visualise it and create models around it for business operations.

 

Data Analyst Courses

Skillfin provides some amazing courses in Data Analyst. Listed below two best courses from them:

1. Bootcamp: The Data Analyst Skills Training (DAST) with Excel

You will learn Data analysis and basically visualization techniques using excel. Mostly taught through live videos. More people have some experience, basically working professionally.

Master the technique of Data Analysis and visualization using excel in this course. All the important tools and skills like Mysql, Tableau, Power BI, Python coding are covered.

• Taught by Mckinsey expert
• Course Duration: 3 months, 50+ skills and pre-recorded videos
• Level: For intermediate people
• Course id divided in total 5 modules

 

Skills you will master through this course:

• Data analysis and its functionalities using Excel
• Creation of Data Automation reports
• Data visualization techniques
• Logical analysis for decision making
• Data cleaning methods
• Pivot Table and analysis using that
• datasheets
• Excel shortcuts on functions
• Excel skills for business
• Data analysis on real life datasets

Prerequisites:

• Nothing. Everything is covered  from scratch.
• Especially designed for  busy professionals.

Key Coverage:

• 1 year Subscription to course is provided
• 50+ Live Online Classes everyday for 3 months
• 6 Hours of Pre-Recorded videos
• Taught by Industry experts
• 1 Dedicated  Mentor for each student
• Assignments assigned and evaluated
• Can be access on  Mobile and Tablet also
• Certificate of Completion provided
• 14 day Money-Back Guarantee if not satisfied

 

Price: Rs 3,999

Module wise distribution:

5. Module 1: Overview of data analytics and MS Excel
5. Module 2: Data cleaning and analysis using TEXT functions
5. Module 3: Data analysis and visualization using basic excel functions
5. Module 4: Data analysis using advanced excel functions
6. Module 5: Advanced data visualization in excel

2. Free Course: Data Analysis using TEXT Functions in Excel

Really good one to understand excel functions for performing data analysis using Excel. Covers almost all the functions of Excel e.g Mathematical skill, formulas, filters, etc.

You will learn some extra coverage and new tips to do data analysis using excel. All the important functions like concatenate, len, Find, left, Mid , Trim and Find functions are covered

You will learn Excel functions to perform data cleaning, data manipulation and data analysis using the Text functions in Excel. This course is more for fresh graduates, since it is a free course. Freshers can take it and learn what data analysis is exactly and how to do it using multiple tools and options and specifically learning it from excel.

5. Complete overview of Microsoft Excel interface
5. Data analysis in Excel
5. Data manipulation in Excel
5. Data cleaning in Excel
6. Application of TEXT functions in data analysis

Key coverage:

5. Overall overview of Microsoft excel interface
5. Data Analysis using Excel
5. Data manipulation using excel
5. Data Cleaning using excel
6. Application and usage of Test Functions in data analysis
5. Complete overview of Microsoft Excel interface

Includes:

5. 1 year access to all its modules
5. Take the program anytime anywhere and access from everywhere
5. 1 hours on-demand video whenever requested
5. 3 Articles for each module
6. 3 Supplemental Resources provided
5. Can be accessed on mobile and TV

 

What does a data analyst do?

Data Analyst in short term means = Data Analyst + Creation of models. It involves tasks which a regular Data Analysis does, and along with it models are also created using AI techniques. All kinds of learning are involved.

 

Data Analyst’s job revolves around:

It involves creating pipelines for data flow from source or enterprise to destination. Destination address can be anything. It can be a database or a visualization tool like Tableau.

It also includes forming a structured data of data. Converting live streaming of data which is non structured and converted into structured format.

Data analysis using Sql or Python programming: You can code with sql queries or Python to do data analysis. Also, you can use MS Excel as well to do data analysis.

Performing visualization using excel, Tableau through charts and graphs

 

Creating models using AI and ML algorithms

It helps with creating models such that it can be used by businesses to increase sales and revenues.

 

Data Analyst job description

The job description of a data analyst varies depending on the companies or projects with which they are associated, but the core needs for all the roles are the same. Core values are listed below:

This includes identification of numerous data sources and data extraction or collection automation.

Performance of data pre processing and sorting of structured and unstructured data.

It helps in analyzing large volumes of data in order to find patterns and trends.

It includes using algorithm and machine learning to build predictive and forecasting models.

Storytelling to non technical personnel or stakeholders utilizing immersive data visualization approaches employing the process known as esembling.

Developing data driven strategies and solutions to address business and handling sensitive information

A data analyst’s job is to ensure that data is processed and sorted correctly in sensitive contexts.

A data analyst must be confident in their ability to spot patterns in data.

They should be able to spot anomalies and perform statistical analysis.

They must have knowledge of Machine learning and be able to apply associated models or algorithms to help machines learn from data.

 

Data Analyst interview questions

Some really asked questions asked in interviews are:

• Enumerate the various differences between Supervised and Unsupervised Learning?
• Selection Bias? What are its various types? explain the goal of A/B Testing
• Sensitivity of machine learning models?
• Difference between overfitting and underfitting?
• Recommender Systems along with an application outlier values and how to handle them?
• Between Python and R, better for text analytics, and why?
• Data cleaning in data analysis.
• Clustering  and systematic sampling?
• Eigenvectors and Eigenvalues
• Compare the validation set with the test set
• Linear regression and logistic regression
• Various steps  in an analytics project.

 

Data Analyst jobs

There are so many job roles for data analysts and many designations are available in the market to appear. Few listed below:

 

Job description

Required to do data mining of unstructured data, apply statistics, ML methodologies to transform into structured data.

 

Requirements:

A Btech Degree in fields like Math, Physics, Computer Analyst, Engineering, etc.

A master’s degree in any field related to Data Analyst

Online certifications are provided.

 

Kinds of Employments:

Full-time: Opted and most common form of employment that ensures stability and career growth in this field.

 

Major recruiting Companies are:

• IBM
• Amazon
• Facebook
• Apple
• Microsoft
• Google
• Avg salary package: India: 8 Lakhs per annum (INR)
• In USA: 85k per annum (USD)

 

Designation: Business Intelligence Developer

Job description

A business intelligence developer is someone who converts business data into understandable format to help the development of the  team. In Real terms, A business intelligence developer is the layman’s data analyst.

He/ She should have practised  BI tools (e.g. Power BI) to understand  the obtained data sets and present them in forms of graphs, diagrams or reports etc. It would be really helpful for business owners and customers to understand clearly.

 

Requirements

B.tech degree in related fields like programming or computer analyst

Any Experience is appreciated in this field.

 

Kinds of Employment

Part-Time: Not recommended and not something for BI developers

Full-Time: Most preferred, since job security is provided.

Famous Recruiting companies:

• Rolls Royce
• Dell
• Amazon
• Microsoft
• Discover
• AVG package: India: 6.5 Lakhs per annum
• In USA: 73.8k per annum (USD)

 

Machine Learning Analyst

Job description

A machine learning engineer/ analyst is highly in demand in this field. He/ She is the one responsible for creation of an algorithm that would access the model, which in turn will provide the given data and produce in the form of a structured output. Educational requirements involve-

• A B.tech degree in fields like Statistics, Engineering etc.
• Extensive knowledge in coding
• Online certifications provided

 

Forms of Employment

Part-Time: For extensive programming skills, who would want to freelance and want to apply from distance.

Full-Time: Preferred by engineers who have a strong technical background and are looking to establish their data analyst job profiles.

 

Famous recruiting companies

• Hotstar
• McAfee
• MakeMyTrip
• Amazon
• Avg package: India: 7.2 Lakhs per annum (INR)
• In USA: 111.5k per annum (USD)

 

How to become a data analyst

There are so many plenty of resources and videos available online and it is very confusing for anyone to start learning all the concepts together. When you are a beginner, it is possible that you might get overwhelmed with so many modules. There is a possibility that you might stop learning due to fear of learning. All I want to say is stay committed.

 

Major things to cover:

• Mathematics
• Python
• R
• SQL
• Data Structure
• Machine Learning
• AI

 

1) Mathematics

Maths is very important as it will help us in understanding various ML algo’s that play a major role in DS

• Chapter to cover in Part 1:
• Linear Algebra
• Analytic Geometry
• Matrix
• Vector Calculus
• Optimization
• Chapter to cover Part 2:
• Logistic Regression
• Dimensionality Reduction
• Density Estimation
• Classification

 

2) Separate coverage to Probability

Probability is also important to statistics, and it is considered a prerequisite for Data Analyst.

Introduction to Probability Part 1:

• 1D Random Variable
• The function of One Random Variable
• Joint Probability Distribution
• Discrete Distribution
• Binomial (Python | R)
• Bernoulli
• Geometric etc
• Continuous Distribution
• Uniform
• Exponential
• Gamma
• Normal Distribution (Python | R)

3) Statistical skills

Introduction to Statistics Part 1

• Data Description
• Random Samples
• Sampling Distribution
• Parameter Estimation
• Hypotheses Testing (Python | R)
• ANOVA (Python | R)
• Reliability Engineering
• Stochastic Process
• Computer Simulation
• Design of Experiments
• Simple Linear Regression
• Correlation
• Multiple Regression (Python | R)
• Nonparametric Statistics
• Sign Test
• The Wilcoxon Signed-Rank Test (R)
• The Wilcoxon Rank Sum Test
• The Kruskal-Wallis Test (R)
• Statistical Quality Control
• Basics of Graphs

4) Coding Practise

You need to have a good grasp of coding concepts like Data Structures , Algorithms. Languages can be Python, R, Java or Scala.

Python Part 1:

• Python Basics
• List
• Set
• Tuples
• Dictionary
• Function, etc.
• NumPy
• Pandas

R Part 1:

• R Basics
• Vector
• List
• Data Frame
• Matrix
• Array
• Function, etc.
• dplyr
• ggplot2
• Tidyr
• Shiny
• DataBase:
• SQL
• MongoDB
• Other:
• Data Structure
• Time Complexity
• Web Scraping (Python | R)
• Linux
• Git tool

References:

• Python
• R
• SQL
• Data Structure

 

5) Machine Learning Algorithms

Introduction to ML Part 1:

• How Model Works
• Basic Data Exploration
• First ML Model
• Model Validation
• Underfitting & Overfitting
• Random Forests (Python | R)
• scikit-learn
• Intermediate:
• Handling Missing Values
• Handling Categorical Variables
• Pipelines
• Cross-Validation (R)
• XGBoost (Python | R)
• Data Leakage

All of the above skills are important and you need to study them in order to grasp them better.

Conclusion:

Overall Data Analyst and Data Analyst differ on the basis of job role. Data analyst is the step 1 towards the data analyst. Data Analyst is for sure entry level job. Data Analyst is more for people having some experience in data related fields. Both are good career roles and you can choose based on your career path.