Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.
Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analysing, extracting information and making informed business decisions through big data.
This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.Extensive hands-on labs and an interview prep will help you land lucrative jobs.
Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn
Understand the behavior of data;build significant models using concepts of Statistics Fundamentals
Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel
Use Python libraries and work on data manipulation, data preparation and data explorations
Use of Python graphics libraries like Matplotlib, Seaborn etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.
3 Months FREE Access to all our E-learning courses when you buy any course with us
Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.
Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.
Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
In this module you will learn how to install Python distribution - Anaconda, basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.
Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario
In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.
You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.
Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.
Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.
Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.
A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).
Bangalore is known as the ‘Silicon Valley of India’. Moreover, it is the most technologically advanced city of the country. It has the most prominent institutes and has some of the biggest companies such as LinkedIn, Amazon, WyngCommerce, Vedantu, Michael Page, Citi, etc. Data scientists have become a necessary asset in every organization in recent times. Although there is no concrete definition of Data science, its impacts around us can be noticed significantly. Data Science can be summarized in five stages of its life cycle which includes the following:
According to reports from LinkedIn, the data scientist is listed as one of the most promising jobs in 2017, 2018 and 2019. Some of the common data scientist job titles are as follows:
The reasons for the popularity of Data Science as a career choice are as follows:
These are just a few examples from our day to day life to show how data science is involved in all aspects. Apart from this, it has revolutionized healthcare significantly, opening new areas of research and discoveries. Its contribution to other fields like wildlife, weather forecast, banking sectors, etc. is also significant.
Bangalore is home to some of the most prestigious universities in the world in terms of Data science courses. These institutions include INSOFE, International Institute of Information Technology, IIK (Indian Institute of Knowledge hub), Peopleclick, Data Science, Data scientist & Data Analytics Courses, Business Analytics Training Institute Bangalore, Indian Institute of Management Bangalore, etc. The top skills that are needed to become a data scientist include the following:
Data Science is a dynamic field with ever increasing tools and technologies added to it every now and then. You should be able to choose the best programming language suited to you to tackle a specific kind of problem. Apart from mathematical skills, it is important to be proficient in one or more programming languages. The programming for Data Science differs from the conventional programming language in the sense that it helps the user to pre-process, analyze and generate predictions from the data, while the other programming languages focus on software development. The main programming languages that an aspiring data scientist should be familiar with are as follows:
2. Big Data:
Big Data technology centers in ways to analyze a large volume of data to reveal behavior, trends, and patterns especially related to human behavior. Big Data Analytics is in the frontiers of IT as it aids in improving business, decision making and providing the biggest edge over the competitors hence it is crucial. Therefore, it is very important to have knowledge about frameworks like Hadoop and Spark that can process Big Data.
Apache Spark is a fast and general-purpose cluster computing system designed to cover a wide range of workloads such as iterative algorithms, interactive queries, batch applications, and streaming. Hadoop provides scalable, reliable, and distributed computing to solve problems including huge amounts of data.
Statistics is a broad field which is defined by Wikipedia as the study of the collection, analysis, interpretation, presentation, and organization of data. The minimum skills needed to make better business decisions from data are descriptive statistics and probability theory. Machine learning requires understanding Bayesian thinking which is the process of updating beliefs as additional data is collected. Key concepts in statistics include:
4. Machine Learning and Advanced Machine Learning:
Machine Learning focuses on the development of computer programs in such a way that they can access data, analyze it and manipulate it so that it provides the ability to systems to automate the experience without the need of programming. Machine Learning requires a better understanding of neural networks, reinforcement learning, adversarial learning, etc. and can be considered as a subset of Artificial Intelligence. The different types of Machine Learning techniques include the following:
It is recommended to have good knowledge of various Supervised and Unsupervised learning algorithms such as:
5. Data Cleaning:
Since the data that the data scientists work on is highly sensitive and important, it is important that the data is correct and accurate before data scientists analyze it and therefore, a considerable amount of time and effort is spent to ensure this. Incorrect or inconsistent data leads to false conclusions hence it has a high impact on the quality of the results. Data quality is defined as validity, accuracy, completeness, consistency, and uniformity of data. The workflow followed for data cleansing includes the following steps:
6. Data Ingestion:
Data Ingestion is the process of accessing and importing data from several different sources into our system for analytics. The sources of data are your IoT Smartwatch, social networks, customer portals, messengers, forums, etc. These are the most common examples of data ingestion :
The different data ingestion tools available :
7. Data visualization:
Data visualization tools provide a better and accessible way to enable decision-makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. It helps to see and understand trends, outliers, and patterns in data by using visual elements like maps, graphs, and charts. By using technology to drill down into charts and graphs for more detail, we can interactively change what data you see and how it’s processed through visualization. A good and effective data visualization tool makes large data sets coherent and some of these tools are as follows:
8. Unstructured Data:
Unstructured data can be defined as data that cannot fit neatly into a database and does not have a recognizable structure. It does not follow the conventional data model like Word documents, email messages, PowerPoint presentations, survey responses, transcripts of call center interactions, and posts from blogs and social media sites. Therefore, it leads to ambiguities that are difficult to identify using conventional software programs. Working with unstructured data provides a better insight into analyzing data.
Below are the top 5 behavioral traits of a successful Data Scientist in Banglaore -
A Harvard Business Review article labeled “data scientist” as the sexiest job of the 21st century. Some of its benefits can be summarised as follows:
Below is the list of top business skills needed to become a data scientist. These skills are a must whether you live in Bangalore or Mumbai:
1. Critical Thinking – Critical thinking involves deliberately and systematically processing information so that you can make better decisions. The role of a data analyst is to uncover and synthesize connections that are difficult to understand.
2. Communication Skills – Data Scientists need to convey their ideas and solutions to other people in a language that is easily understood by everyone. He/She must possess good communication skills. Most of the presentation is done in the form of charts, graphs, figures, and statistics. It is important to simplify it since a team includes people from different areas.
3. Business acumen – The business requirements of different companies are different. It depends on a number of factors. The solutions or ideas proposed by you affects the business, sometimes on a very broad scale. Therefore, it is important for you to know the objective of the business and the impact you are going to create through your contributions.
4. Presentation Skills – A data scientist works in a team of people with different roles. He/ She needs to deliver a speech or a presentation in front of his/ her team, clients, or any stakeholder. Therefore, it is important to have good presentation skills.
The 5 best ways to brush up your Data Science Skills to get a Data Scientist job are as follows:
In India, Data Science is a lucrative career option. Every sector and organization is inviting candidates based on their requirements. Bangalore has some of the biggest companies such as LinkedIn, Amazon, WyngCommerce, Vedantu, Michael Page, Citi, etc which offer jobs to Data Science professionals.
The kind of companies that employ data scientists are as follows:
The three general and basic steps to become a data scientist are as follows:
Next, you should focus on to develop an in-depth skill and knowledge in all or some of the following technologies:
The job of a data scientist is challenging and highly in demand. There are different skill sets required by different companies still there are some general steps to be followed by everyone who aspires to become a data scientist.
1. Degree: You must hold a basic engineering or related degree in Computer Science, IT, Mathematics, etc.
2. Certificate: It is advised to get certification in order to enhance your profile. It also confirms that you are proficient enough in a particular field. Below are some of the certifications available:
3. Technical skills: You must aim to master one or more of the most emerging technologies of Data Science. You can choose this aspect based on your interest and your desired job profile. These technologies are as follows:
There are many advantages to having a degree in Data Science. A data scientist with degree can-
Generally speaking, it is not an essential criterion but still, there are certain points to be taken care of. You can go for a Master’s degree based on the role and the company that you are focusing on.
If the product of the company is solely based on Data Science, then the expectations are really high and such companies demands for a Master’s degree. The role of the data scientist is very crucial in such companies. For example, in cybersecurity, fraud detection is based on Data Science. If you wish to work in data-based companies like Google and Facebook, then having a Master’s degree is a bonus.
In some companies, Data Science is used as a way to provide insights to other teams or to enhance the core product like the product, sales, and marketing teams. For example, a company like Target use data science to predict how much inventory to stock in different stores. In such companies, the Master’s degree is not a necessary factor.
A programming language is a key skill that a Data Scientist must possess. You must be proficient in one or more of the following programming languages.
The average annual salary of a Data Scientist in Bangalore is Rs. 6,15,496.
The average salary of a Data Scientist in Bangalore is Rs. 6,15,496 as compared to Rs. 6,13,889 in Hyderabad.
A data scientist in Bangalore earns about Rs. 6,15,496 every year as compared to Rs. 6,72,492 in Mumbai.
The annual earnings of a data scientist in Bangalore is Rs. 6,15,496 as compared to Rs. 8,19,815 in Chennai.
There is a high demand for Data Scientists in Bangalore. Lot of companies are trying to leverage the abundant data that is being generated each day and this has created huge job opportunities for Data Scientists in Karnataka.
Data Scientist is one of the hottest jobs right now. If you are a data scientist in Bangalore, you will get several opportunities to work and grow in your career owing to the presence of major players like Accenture, Infosys, etc. and also the numerous startups that are present here.
Bangalore is the best place to work if you are looking for growth. The city is home to several startups that offer multiple opportunities to freshers as well as experienced employees. Data Scientists also get to gain the attention of executives as they play a key role in determining useful business insights. In this field, many certifications are not required as you will learn on the job with time. Also, a data scientist is not bound to work for a particular business alone. You can use this new technology with enormous potential in any field that interests you.
The companies hiring Data Scientists in Bangalore are DigiSciFi Technologies, SAP Labs India Pvt Ltd, Intellicar Telematics Pvt Ltd, Accenture Solutions Pvt Ltd, People Source Consulting Pvt Ltd, and many more.
|1.||Machine Learning Developers Summit 2019, Bengaluru, India||30-31 January, 2019||NIMHANS Convention Centre, Hosur Main Road, Lakkasandra, Hombegowda Nagar, Bengaluru, Karnataka 560029|
|2.||Open Data Science Conference, Bengaluru, India||7-10 August, 2019||Sheraton Grand Bangalore Hotel, A Block, 26/1, Dr. Rajkumar Rd, Rajaji Nagar, Bengaluru, Karnataka 560055|
|3.||Data Platform Summit 2019, Bengaluru||22-24 August, 2019||Hotel Radisson Blu (Formerly Park Plaza), 90/4, Marathahalli Outer Ring Road, Bengaluru, Karnataka 560037, India|
|4.||Future of Analytics Summit 2019, Bengaluru, India||27 February, 2019||The Ritz-Carlton, 99, Residency Rd, Shanthala Nagar, Ashok Nagar, Bengaluru, Karnataka 560025|
|5.||Great International Developers Summit, Bengaluru, India||22-25 April, 2019||IISc Bengaluru, National Science Seminar Complex, CV Raman Rd, Kodandarampura, Malleshwaram, Bengaluru, Karnataka 560012, India|
|6.||The Fifth Elephant, Bengaluru, India||25-26 July, 2019|
NIMHANS Convention Centre, Hosur Main Road, Lakkasandra, Hombegowda Nagar, Bengaluru, Karnataka 560029
|7.||CYPHER 2019||18-20 September, 2019||TBA|
|8.||Artificial Intelligence and Machine Learning Summit 2019 - Bangalore||23 May, 2019||Hyatt Centric Mg Road Bangalore, Swamy Vivekananda Road, Someshwarpura, Ulsoor, Bengaluru, India|
|9.||Bengaluru Tech Summit 2019||18-20 November, 2019|
Bengaluru Main Palace, Bengaluru, Karnataka, 560052, India
1. Machine Learning Developers Summit 2019, Bengaluru, India
2. Open Data Science Conference, Bengaluru, India
3. Data Platform Summit 2019, Bengaluru, India
4. Future of Analytics Summit 2019, Bengaluru, India
5. Great International Developers Summit, Bengaluru, India
6. The Fifth Elephant, Bengaluru, India
7. CYPHER 2019,Bengaluru, India
8. Artificial Intelligence and Machine Learning Summit 2019, Bengaluru, India
9. Bengaluru Tech Summit 2019
|1.||DataHack Summit 2017||9 – 11 November, 2017||MLR Convention Center, Whitefield, Bengaluru|
|2.||The Fifth Elephant 2017||27-28 July, 2017, Bengaluru||Dyvasandra Industrial Layout Mahadevapura, Whitefield, Kaveri Nagar,|
|3.||NASSCOM Big Data & Analytics Summit 2018||Jul 11, 2018 - July 12, 2018||Taj Yeshwantpur, 2275, Tumkur Road, Yeshwanthpur, Bengaluru, India|
1. DataHack Summit 2017, Bengaluru, India
2. The Fifth Elephant 2017, Bengaluru, India
3. NASSCOM Big Data & Analytics Summit 2018, Bengaluru, India
The logical sequence of steps you should follow to get a job as a Data Scientist is as follows.
The 5 important steps to prepare for data scientist jobs are as follows:
Data scientists are vital to companies. They take an enormous mass of unstructured and structured data points and use their formidable skills in math, statistics, and programming to clean, arrange and organize them. Then they apply all their analytic powers like industry knowledge, contextual understanding, skepticism of existing assumptions to provide hidden solutions to business challenges. Some of the responsibilities can be listed as follows:
The average salary for a Data Scientist in Bangalore, Karnataka is Rs 902,866 per year.
The various steps in the career path of a Data Scientist in sequential order is given as below:
Below are the top professional organizations for data scientists in Bangalore –
Some of the other ways to network with data scientists to fill potential employees are as follows:
There are several career options for a data scientist in Bangalore –
Bangalore has some of the biggest companies such as LinkedIn, Amazon, WyngCommerce, Vedantu, Michael Page, Citi, etc which offer jobs to Data Science professionals. The offer high salaries but also demand in-depth knowledge in the field.
Here are the key points, which the employers generally look for while hiring data scientists:
Python allows to explore the basics of machine learning and makes it easy and effective. Machine learning is more about statistics, optimization, mathematical and probability. Some of the reasons why Python is considered as the most popular language to learn Data Science are as follows:
As data science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language.
Follow these steps to successfully install Python 3 on windows:
Note: You must ensure to check the box that says Add Python 3.x to PATH as shown to ensure that the interpreter will be placed in your execution path.
To install python 3 on Mac OS X, just follow the below steps:
$ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
$ brew install python
Knowledgehut is the best training institution which I believe. The advanced concepts and tasks during the course given by the trainer helped me to step up in my career. He used to ask feedback every time and clear all the doubts.
I would like to thank KnowledgeHut team for the overall experience. I loved our trainer so much. Trainers at KnowledgeHut are well experienced and really helpful completed the syllabus on time, also helped me with live examples.
I would like to extend my appreciation for the support given throughout the training. My special thanks to the trainer for his dedication, learned many things from him. KnowledgeHut is a great place to learn and earn new skills.
The workshop held at KnowledgeHut last week was very interesting. I have never come across such workshops in my career. The course materials were designed very well with all the instructions. Thanks to KnowledgeHut, looking forward to more such workshops.
The trainer took a practical session which is supporting me in my daily work. I learned many things in that session with live examples. The study materials are relevant and easy to understand and have been a really good support. I also liked the way the customer support team addressed every issue.
KnowledgeHut has all the excellent instructors. The training session gave me a lot of exposure and various opportunities and helped me in growing my career. Trainer really was helpful and completed the syllabus covering each and every concepts with examples on time.
I was totally surprised by the teaching methods followed by Knowledgehut. The trainer gave us tips and tricks throughout the training session. Training session changed my way of life.
My special thanks to the trainer for his dedication, learned many things from him. I liked the way they supported me until I get certified. I would like to extend my appreciation for the support given throughout the training.
Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.
Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. Payscale.com puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.
By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.
Tools and Technologies used for this course are
There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers virtual training.
On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.
Your instructors are Python and data science experts who have years of industry experience.
Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.
In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.
Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor