What is Data Science
Data Science is the application of Machine Learning Algorithms and Statistics over large volumes of Structured and Unstructured Data to derive patterns and rules which could be further used to predict outcomes for a given set of inputs (read data).
In 2015, the American Statistical Association identified database management, statistics and machine learning and distributed and parallel systems as the 3 emerging foundational professional communities.
Who is a Data Scientist?
Data Scientists are professionals who are skilled in Organising and analysing vast amounts of Data. Data Scientists are well versed with the Machine Learning Algorithms, Data Visualisation and their application in Solving very Complex Problems.
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A professional needs to acquire the following skills to become an effective data scientist
- Programming
- Statistics
- Machine Learning
- Linear Algebra and Calculus
- Data Visualization
- Communication
- Data Wrangling
- Data Intuition
- Software Engineering
One of the things that differentiates a good data scientist is their ability to differentiate the points that are Critical to a Project vs what are not. To arrive the same, Data Scientists need to be able to identify relevant questions, collect data from a number of different data sources, organise the data, translate it to the solution and communicate the findings. All this leads to effective business decision making. These skills are required in any industry, making Data Scientists very valuable to companies.
An Effective Data Science Course is one, which trains professionals on all the above mentioned skills.
Why Become Data Scientist
The demand for Data Scienists is steadily increasing across the globe. The days are gone, where organizations needed these skills to buid a competitive advantage, however, we are progressing in a world where the survival of organizations would depend upon the decisions made using Data Science. In this world,
Data Science Career Outlook and Salary Opportunities
In our humble opinion and going the progress made in the field of Data Science on a daily basis, we expect the demand for Professionals who have undergone a training in Data Science to increase exponentially in 2020-21. As of January 2020, there are 5,200 open positions listed only on GlassDoor, which is 21% more than Jan 2019. The average salaries quoted by GlassDoor for a Data Science Professional is as follows • Data analyst: $65,470 • Data scientist: $120,931 • Senior data scientist: $141,257 • Data engineer: $137,776Join the best-in-class courses by leading faculty and industry leaders.
How does one become Data Scientist
Considering the number of areas to be covered to become a Data Scientist, it is suggested that one avails the required guidance from Trained Professionals.
The most preferred route to becoming a Data Scientist is to go through a Class Room Coaching in Data Science. Devu.in provides Certified Data Science Course via ClassRoom Coaching in HSR Layout, Bangalore and Live Online Training in Data Science. The Live Online course can also be availed in their ClassRooms in their Centers in Rammurthy Nagar and Noida.
There are, of course, other options for one to become a Data Scientist via Self-Study, MOOC and Youtube videos, You could see the comparison and benefits of each of these modes of access in a section of devu.in
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Partial List of Topics Covered in Data Science Certification Course in Bangalore at devU.in
Data Science Overview
Introduction to Data Science |
Different Sectors Using Data Science |
Purpose and Components of Python |
Statistical Analysis
Introduction to Statistics |
Statistical and Non-statistical Analysis |
Major Categories of Statistics |
Statistical Analysis Considerations |
Population and Sample |
Statistical Analysis Process |
Data Distribution |
Hypothesis testing |
Numpy
Pandas
Machine Learning |
| Machine Learning Approach |
Supervised Learning Model |
| Regression Linear regression |
| Multiple Linear Regression |
| Multiple Linear Regression |
| Classification |
| Logistic regression |
| SVM |
| Naive Bayes |
| Decision Tree |
| Random Forest |
| K Nearest Neighbors |
Unsupervised Learning Models |
| K-means Clustering |
| Apriori association rules |
| Sklearn for machine learning model |
Data Visualllization with MatPlotLib |
| Introduction to Data Visualization |
| Line Properties |
| Scatter plot |
| Bar graphs and histograms |
| Pie charts |
| Subplots |
FAQ
What is data science?
Data Science is a field, that uses Scietific Methods, Processes, Algorithms and Systems to extract knowledge and insight from data.
What is a Data Scientist?
Data Scientists are people with some mix of coding and statistical skills who work on making data useful in various ways
Does Data Science Requires Coding?
Yes, it does. You could do in C++, Java, R or Python, however, Python is the most common language for Data Science
What Programming Language is helpful for Data Science
Python and R are the preferred languages for Data Scientists
What skills do I need to become a Data Scientist?
The Skills required to become a Data Scientist are, Programming Skills, Analytical and Data Interpretation Skills, Business Communication Skills, Mathemtically and Statistical skills
What is the best way for somebody to get started on Data Science?
There are lots of resources online. You could also take a certification course with Coursera or edX. However, if you want a disciplined approach to Data Science, you coud go for a ClassRoom course with devU.in
Is Python better or R better for Data Science?
Python and R are both equally good for Data Science, however, if you had to deploy your models, Python would be a better choice, due to the large number of frameworks available in Python
How do I become a Data Scientist?
Data Science is a culmination of Stats, Maths, Programming, Data Management and Modelling. Unless you have a good grounding in each of these areas, you are unlikely to succeed as a Data Scientist. You could take an online course to excel these skills, or else, tkae a course with devU.in
Do I need a Masters/PHD to become a Data Scientist?
No, you don't
Will I be able to work Overseas as a Data Scientist?
Absolutely, with a significiant skill-gap in the area of a Data Science across the globe. Having Skills in data science is absolutely a plus for you to work as a Data Scientist anywhere in the world
In the Context of Data Science Online Course, what is logistic regression?
Logistic Regression often referred as logit model may be a technique to predict the binary outcome from a linear
combination of predictor variables. for instance , if you would like to predict whether a specific politician will win the election or not. during this case, the result of prediction is binary i.e. 0 or 1 (Win/Lose). The predictor variables here would be the quantity of cash spent for election campaigning of a specific candidate,
the amount of your time spent in campaigning, etc.What are Recommender Systems?
In the Context of Artifical Intelligence Training, Recommender Systems are one of the information filtering systems that are meant to predict the user’s next choice based on the past choices. Recommender Systems are widely used by ecommerce sites, like Amazon and Flipkart, in recommending products to customers and by sites like Netflix, Spotify in recommending Movies and Songs to the their users.
In a Data Scientist’s role, what data cleaning plays in analysis?
Once you have completed a Data Science Training and are required to handle Data Science Projects, one of the roles that you need to do it cleaning up data from to transform it into a format that data analysts or data scientists can work with. It is a tedious process because the time taken to clean the data increases with the number of data sources and the volume of the data generated in these sources. For some projects, data cleansing consumes approx 75% of the time in the project
In the Context of Data Science Training, Differentiate between univariate, bivariate and multivariate analysis.
This topic would be covered in details in Descriptive Statistical Analysis Training in Data Science training:
- Univariate Analysis: The analysis on the effect of 1 Variable on a desired outcome can be referred to as a univariate analysis. The most simple example is Pie Chart.
- Bi-Variate Analysis: The Analysis on the effect of 2 Variable on a desired outcome is termed as Bi-Variate Analysis. A common example is ScatterPlot.
- Multi-Variate Analysis: The Analysis of more than 2 variables on a desired outcome is termed as Multi-Variate Analysis.
In Data Science Certification, what are various steps involved in an analytics project?
- Step1: The Data Scientist needs to understand the business problem
- Step2: The Data Scientist needs to explore the data and become familiar with it.
- Step3: The Data Scientist then prepares the data for modelling by detecting outliers, treating missing values, transforming variables, etc.
- Step4: The Data Scientist then starts running the model, analyzes the result and tweaks the approach. This is an iterative step till the best possible outcome is achieved.
- Step5: The Data Scientist then validates the model using a new data set.
- Step6: The Data Scientist finally starts implementing the model and tracks the result to analyse the performance of the model over the period of time.
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Course Features
- Students 1256 students
- Max Students50
- Duration80 hour
- Skill levelintermediate
- LanguageEnglish
- Re-take course2
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