There has never been a better time to learn data analysis. This will not only upgrade your value in the job industry, but will also make you grow across towards the field of data science.

Even at entry level, data analysis will boost your salary. According to IBM, the number of jobs for data professionals in the U.S will increase to 2,720,000 by 2020.

Currently, the need of data analysts is more than the available supply, and companies are willing to welcome students and professionals to fill their positions. Talking in terms of data, the following job profiles required extensive skills in the field of data analyst.

IT System analyst: A typical systems analyst in the US makes around $68,807.

Healthcare analyst: The median salary for a Healthcare Data Analyst is $61,438.

Operations analyst: Average salary of $55,981.

Business analyst: Average salary of $55,000.

Data scientist: The average data scientist salary is $113,436, according to Glassdoor.

Data engineer: median salaries being comparable to data scientists at $90,963.

Quantitative analyst: The median salary for quantitative analysts is $82,879.

Data Analytics Consultant: $78,264 is a representative salary for the role.

Digital Marketing Manager: median salary is around is $80,000.

Project Manager: A typical salary for a project manager is around $73,247.

Transportation Logistics Specialist: professionals in this industry makes around $79,000 per year.

There are endless profiles, where you can implement data analysis. Working with data is a necessary skill in every domain of professionalism. Whether you are in the field of finance, engineering, medical, marketing, and commerce or else, every professional requires to work with data, and with this, every professional, must be skilled enough to work with data and analyse them to retrieve essential information and make decisions.

This course understands the ongoing market trends. With this, this course provides the ultimate specialization package for “data analytics with python”. What this course offers?

To start from scratch! And make you gain all major skills to be a successful data analyst.

To cover all basics, necessary for data analysis.

To cover the most important skill to be a data analyst: Python!

To cover the basics and intermediate topics of python, essential for understanding the functions and libraries for data analysis.

To understand and work all major libraries of data analysis.

To understand and work on the concepts of data wrangling, data filtering, data cleaning, data transformation, shaping, combining, plotting and aggregation.

To work with practical example and understand all major functions you require to analyse data.

To combine the knowledge of python with data analysis and use the built in data structures to understand and manipulate data.

To make you understand the data. To make you understand the requirements and to make you experience, how you can use your domain to upskill your job opportunities.

To give you the necessary skills, if you want to pursue higher education the field of data science, machine learning, neural networks and statistics.

In short, to make you an ideal data analyst!

Data Analytics in India!

More than 97,000 analytics positions remain vacant in India due to the shortage of talent.

Opportunities for freshers have also increased with openings for them accounting for 21% of analytics jobs, compared to 17% last year.

Hari Krishnan Nair, Co-founder, Great Learning said:

A 45% increase in the supply gap in just one year indicates the pace at which businesses are adopting analytics and data-based decision making.

According to the report, the median salary being offered for analytics jobs in India is INR 11.5 lakhs/annum.

Around 23% of all advertised jobs last year were offering more than INR 15 Lakhs as opposed to 20% a year ago.

According to the report, demand for Python is the highest amongst all the recruiters.

Currently 45760 jobs posted in Naukri.com for data analysts in India.

Data analysts with more than 5 years of experience often earn up to 15 lakhs per annum. Senior data analysts with more than 10 years of experience could earn above 20 lakhs per annum. .

How to achieve all this? We have the answer! DIYGuru provides the ultimate specialization package with 7 best courses! Yes, you heard that right! Specialization course with 7 courses. Covering each major domain of python and data analysis, taking you right from ground zero, to the sky (with no limits!)

This course assumes that you currently holds little or no knowledge in python and data analysis. So suited best for beginners. Courses description is as well:

Course 1: Introduction to Python and Data Analysis

In this course, students will be introduced to the python programming language and the concept of data analysis. How data analysis has become one of the most important skills for students in different areas. How data analysis and Python, is useful in every domain, ranging from mathematics to physics, from computer science to electrical engineers. Every second company works with data, and so, requires, professionals, who can understand their data and can play with them! This course will bring out the understanding and motivation to start a career in data analysis using python.

Module 1: What is python and why it is so famous?

In this module, students will come to know about python, as a programming language, and how, it turned out to be one of the most important languages in the world. This module will help students to understand the language from scratch, irrespective of their past professional experience.

Module 2: What is data analysis?

• In this module, students will see what exactly a data is, for an engineer. And what kind of data suits your needs. How to get the understanding, in order to get the required hidden information from any level of datasets. This module will also discuss the skills required for anyone interested in pursuing a career in this field.

Module 3: Why Python for data analysis

• • In this module, how a programming language enables us to understand the data and retrieve the information we want. Students will understand why python is the most used language and how python is one of the best available languages for implementing data analysis

• Module 4: What data analysts do?

• • • In this module, students will understand the average profile of a data analyst. Students will be shown how the implementation of data analysis changes with the different field, a student is working on. This virtual tour will help students to correlate themselves with different domains of work areas.

• Module 5: Application areas
  • • • In this module, we will discuss the numerous ways, a student could play with data. We will discuss different job domains and their growing needs for data analysts.

• Module 6: Necessary pre-requisites and software requirements

• • • In this module, we will discuss, the basic mathematical understanding, the student should have, before beginning with data analysis. However, his course, consider assumes that student has nearly zero knowledge of python language. We will discuss the necessary tools and software as well.

Course 2: Fundamentals of Python

• • This course will focus on the basics of the python language. Since python is currently the most used language for data analysis, it is essential to cover all basics of python language. Students will go through the following:
    • Understanding programming
    • Terminology
    • Writing basic programs
    • Building blocks
    • Variable and types
    • Keywords
    • Statements, operators and operands
    • Expressions
    • Multiple operators
    • User input
    • Execution
    • Conditionals

Course 3: Python Intermediate

• • • Working with python basics will give students to flexibility to upskill and upgrade their python knowledge to higher level. This will give you more understanding of analysing and manipulation data with python concepts in more detail. The topics to be covered are:
      • Functions
        • Built in functions
        • Type conversion
        • New functions
        • Execution flow
        • Parameters and arguments
      • Loops
        • While loop
        • Using ‘for’
        • Loop patterns
        • Using ‘continue’
      • Strings
        • Operators in strings
        • Looping in strings
        • Parsing strings
      • Files
        • Reading and opening files
        • File search
        • Writing files
      • Lists
        • Traversing a list
        • List operations
        • Working with elements
        • Lists with functions
        • Lists with strings
        • Aliasing
        • Objects and values
      • Dictionaries
        • Dictionaries with files
        • Text parsing
        • Debugging
        • Looping with dictionaries

Course 4: Python Advanced

• • This course will introduce the data analysis and revise the python data structures and accessing them via data analysis tools. Students will cover the following:
    • Language semantics and control flow
    • Running Jupyter notebook
    • Data structures
    • Tuples
    • Built in functions
    • List, set, dict
    • Functions
    • Error handling

Course 5: Data Analysis Fundamentals (Numpy)

• • One of the most important libraries to be able to understand data is the library Numpy. Understanding data in the form of arrays and accessing them and manipulating them in array form will give you the foundation to learn higher level libraries in coming courses. More details to be updated soon.
  • More specifically, students will cover the following:
    • The NumPy ndarray:
    • Data types
    • Indexing and types
    • Slicing technique
    • Array transformations and swapping
    • Logic and methods with array
    • Element-Wise Array Functions

Course 6: Data Analysis Intermediate (Pandas) 

• • Simply the heart of data analysis. The main library to perform data wrangling, combining, reshaping, transforming, filtering, and manipulating. Actual analysis comes here! The following topics will be covered in detail:
    • Series
    • DataFrame
    • Indexing
    • Essential functionalities
    • Selection and filtering
    • Sorting and data alignment
    • Correlation and covariance
    • Data loading
    • Data preparation
    • Missing data and duplicates
    • Transformation
    • Working with strings
    • Filtering missing and unused data
    • Data wrangling
    • Working with multiple datasets
    • Merging, combining and concatenating

Course 7: Visualisation using Python in Data Analytics

• • Make your data talk! Visualize the data and make them communicate with others. Plotting and analyzing them is the best way to show your analysis and decisions. The topics, to be discussed will be:
    • Plots and figures
    • Markers, labels, annotations
    • Configuring matplotlib
    • Line plots
    • Bar plots
    • Histogram and scatter plots
    • Categorical data
    • Other tools