Python for Data Analysis and Visualization Training

Python for Data Analysis Career Path & Qualifications. Skilled data analysts are some of the most sought-after professionals in the world. Because the demand is so strong, and the supply of people who can truly do this job well is so limited, data analysts command huge salaries and excellent perks, even at the entry level.

For several years data scientist has been ranked as one of the top jobs in the US, in terms of pay, job demand, and satisfaction. But there are signs the coveted role may be losing some of its sheen, as salaries for data scientists begin to plateau.

Python's focus on simplicity and readability, it boasts a gradual and relatively low learning curve. This ease of learning makes Python an ideal tool for beginning programmers. Python offers programmers the advantage of using fewer lines of code to accomplish tasks than one needs when using older languages.

We are happy and proud to say that we have strong relationship with over 700+ small, mid-sized and MNCs. Many of these companies have openings in Python for Data Analysis and Visualization . Moreover, we have a very active placement cell that provides 100% placement assistance to our students.

• Step 1: Learn Python Fundamentals
• Step 2: Practice Mini Python Projects
• Step 3: Learn Python Data Science Libraries
• Step 4: Build a Data Science Portfolio as you Learn Python
• Step 5: Apply Advanced Data Science Techniques

Difference Between Data Visualisation and Data Analytics. Data visualization is nothing but, representing data in a visual form. ... Data analytics is the method of examining data sets (structured or unstructured) in order to get useful insights to draw conclusions about the datasets.

The two most conducive languages are Python and R. Python is a general purpose productive language, that has somehow resulted in an excellent option to do data science. It can be used for much more than that. Python has pretty good packages and nlp kits for data learning.

Data Science Python is widely used and is a favorite tool along being a flexible and open sourced language. Its massive libraries are used for data manipulation and are very easy to learn even for a beginner data analyst.

There are a lot of estimates for the time it takes to learn Python. For data science specifically, estimates a range from 3 months to a year of consistent practice.

Python is a general-purpose programming language, meaning it can be used in the development of both web and desktop applications. It’s also useful in the development of complex numeric and scientific applications. With this sort of versatility, it comes as no surprise that Python is one of the fastest-growing programming languages in the world.

• 1. Ease of Learning
• 2. Faster Development and Processing
• 3. Powerful Packages
• 4. Community Support
• 5. Better Data Visualisation
• 6. Compatible with Hadoop