Data science course in Bangalore
Recent advancement in Artificial intelligence and machine learning has led to a lot of searches for data science course in Bangalore. At Programink Bangalore, we provide an end-to-end solution for data science training.
What is Data science and what does a Data scientist do?
Gathering, analyzing and solving a problem with the help of data and algorithms is the crude definition of data science. Just like any other branch of science, Data Science heavily consists of experiments, research and ground breaking findings. It is the intersection between Mathematics and Computer Science and hence the application of data science in today’s tech heavy world is endless. You can be sure that some of the major problems in e-commerce, pharmaceuticals, banking, logistics, medicine, astrophysics etc. are being solved with the help of data science. Turning award winner Jim Grey famously described Data Science as the “fourth paradigm of Science” with empirical, theoretical and computational being the first three.
Data science training in Bangalore
If you are looking for data science jobs or internship with training starting from scratch, covering python, statistics and everything else in the way, you are come to the right place at Programink for data science training in Bangalore.
Why Programink is the best data science training institute in Bangalore?
At Programink we truly believe in delivering quality content which are up to the mark with the current market standards with the Chinese philosophy that "There are no bad students, only bad teachers". So if you have willingness to learn and an unapologetic attitude to achieve your own commitments then Programink is the right choice for you.
We’ve broken down the data science syllabus into 6 electives, as mentioned next.
Elective 6: Capstone project
Data science courses
Learn everything from scratch to advance.
Applied Data Science Fundamentals
- Duration: 4 weeks
- Difficulty: Low
Full-stack Data Science
- Duration: 100 days
- Difficulty: Medium
- Placement Guarantee Program
Course Name: Applied Data Science Fundamentals
Module 1: Analytical thinking
A walkthrough of course, lab setup and guesstimate question to provoke analytical thinking. After setting up all the required tools and environments this modules kicks off by building the analytical and quantitative thought process which you would need throughout this course and subsequently in your data science career.
Module 2: Data analysis using Excel
Explore excel features and formulas for data analysis. The potential of Micosoft Excel would be displayed in full throttle as we learn it from basics to advance. After learning the functionality of this tool we will straight away jump into the practice question which would form the base of the capstone project
Module 3: Data mining from SQL databases and MongoDB
Analysis begins with data. Get your data from different databases and cloud environments. As this modules takes you through the first step in Data science which is data mining, we make sure that all the techniques and knowledge are delivered in the most hands on way possible. At the end of this module you would comfortable with both relational database and Non-relational database query languages.
Practice 1: Case study
A case study would be discussed with the mentor where all the concepts and skills that are taught so far would be aggregated and put to use. Once completed another case study on similar guidelines will be given as assignment to the students. The students would be graded quantitatively based on their submissions.
Module 4: Python fundamentals
Learn python basics, operators, data types and comprehensions. In this module we start from the scratch and introduce you to the python programming language. Starting off with some tips on the notebook where we write most all of our code we would take you through some basic mathematical operations that you can perform in python. In this module, we would also look into different types data structures and sequences in python.
Module 5: Numpy
Get better at working with higher dimension data using Numpy. A data scientist works mostly with the data and numpy (Numerical Python) is the core library in python. It supports n-dimensional array, random number, linear algebra etc. So we would cover this package extensively.
Module 6: Pandas DataFrames
Use Pandas library to read CSV data, make data frames and perform analysis. Pandas in python is extensively used to manipulate, wrangle & read data. We would be learning it in real time while working on a data set.
Practice 2: Case study
A case study discussion with the trainer and project mentor
Module 7: Data visualization with Matplotlib and Seaborn
Visualize larger datasets with different graph forms and markers. What good is a knowledge which cannot be communicated in an efficient and simple way, we as a data scientist need to learn how to present their insights and findings on the platter so that it can easily be digested by our stakeholders. Hence, we would intensively dive into the challenge of presenting the insights and strategies in graphical form.
Module 8: Exploratory data analysis
Perform initial investigations on data to discover patterns, spot anomalies while reshaping the data. Using previously learned tools we would present some numerical and graphical trends in the data. We would also learn some basic analysis that is needed to fully understand the data which is in hand. In addition to all these, some basic techniques will also be covered which would help you sharpen your analytical thinking.
Module 9: Statistics
Learn core statistics concepts and hypothesis testing. As statistics is the back bone of data science hence we would explore all the basic but necessary concepts of statistics. We would be using previously learned tools to perform basic statistical analysis on the data and connect the concepts of statistics with real time cases.
Practice 3: Summary
Summarization of learning so far by the training mentor.
Module 10: Linear Regression
Learn the concepts of supervised machine learning and develop a regression model. This would be your first step in the world of machine learning in Data science subject. You would be introduced to the business cases where this techniques is applied. We would be covering all the aspects of when & how around this technique and used all our skills to develop a model which will predict the dependent variable.
Module 11: Sci-kit learn algorithms and metrics
Understand the use of Scikit-learn library in machine learning models and different metrics use to evaluate the performance of machine learning models. As you would have already built a regression model you would need to know whether our model is robust or not, Sklearn is precisely the package which would help us determine how good our model is. We end this module with the introduction to the capstone project that we would be working on in the next module.
Module 12: Capstone project
An industrial project on big datasets using multiple linear regression. This project would demand all the concepts to come in action which were delivered in the previous weeks. Once completed students would be given similar project from a different industry as an assignment and their submission would be graded.
Data science projects
Get certificate and project internship
Use case: Formulate strategies in accordance with feedbacks and rating available on Data science training in Bangalore.
About: Business always grows when it listens to its customers. But how can we even understand those opinions if they are in different forms likes rating stars, text, likes, shares etc. and are coming in the database from thousands of unique customers every day? Well, to solve this business problem we put a model in place which would constantly consume the different forms of feedbacks and tell us whether, as of today, our customer base is happy, sad, neutral or angry about our products and services.
Implementation: This project can be implemented on different verticals of the business. For e.g. to answer the question, what are our customers’ sentiments towards our Customer care executives? Various type of sentiment analysis models can be built. Suppose, we are required to find the polarity of the sentiments of the reviews then we would build our model around fined-grained sentiments analysis, if we want to classify the response of the customer as happy or sad then we will build aspect-based sentiments analysis model. Putting a sentiment analysis model in place for our Customer care executives can facilitate them in devising proactive and reactive measures based on their performance when it comes to handling inquires.
Algorithms: We would be doing this project in Python and we would be using SVM as the algorithm to classify the feedbacks. We would also explore some NLTK packages which would support us in cleaning the data for building our model. On visualization part we would explore word clouds bubble plot etc. to make inferences.
Use case: Minimizing the uncertainty of returns to mitigate the financial risks in the stock market.
About: If you could know when price of particular stock is going to fall you are most likely to sell your shares and exit the market. But how would you know whether the market would go bull or bear in the near future. That’s where predictive models comes in handy. You can levy computer to study the market trends and behaviours and take your data backed decisions to sell or buy shares. Predictive models are also used to study the future behaviours of SKUs given the effects of seasonality, trends and irregularities.
Implementation: Apart from the mentioned use case predictive model can also be used in finding out the impact of product promotions and prices on sales. Where sales being the dependent variable we would use all the significant independent variable to find out how does our sales value behave if we tweak out independent variables like price and promotions. Another implementation would be to predict the inventory required given the supply and demand of a product. If we could manage our inventories efficiently then we could not only save cost reverse supply chain cost but also optimize our distribution model.
Algorithms: Regression algorithms works like a charm in this kind of model. We would also cover PCA to find out which independent variable should be considered while building a predictive model.
Use case: What are the benefiting courses that can be recommended to a student who has completed basic python course?
About: Cross sell and up sell are among the most useful strategies when it comes to expand the height and width of customer purchase portfolio. You can either pitch a customer similar product or sell a product which is in the same vertical but ranks higher. To know which product to recommend, a recommender model is required. This model would help us narrow down on products which a customer is most likely to buy given his/her previous purchase history or personal attributes.
Implementation: Believe it or not but if you have shopped online recently then you have come across the recommender models work that was put in place by the organization you were engaging with. The ‘suggested items’ and ‘people also bought’ section is where this model shines. For e.g. If a customer walks in a groceries store and buy a milk packet then our recommender model would find the product which was bought most number of times whenever milk was bought. In this case it might recommend bread to the customer if that is currently missing from his/her basket. Another great example would be Netflix movie recommendation which suggests users what movies or TV shoes they might like. Collaborative, content based and hybrid methods could be used to build a recommender model, the right choice always depends on the given business case.
Algorithms: To cluster similar products we could use Kmeans technique or Association rules. Once we have identified the clusters we can recommend products from the same cluster to the customer or if we have no previous history of purchase then we could use Bayesian Classifiers or decision trees on our existing customer base to find which cluster does the new customer belongs to given his/her basic attributes like age, gender or ethnicity.
Use case: The business wants to identify its customer base so that it send personalised email and SMS to them.
About: To optimize the experience of a user, first of all, you would like to know who that user is and in which category does he fall. Once that is determined multiple strategies can be formed that would be tailor made for that user and others who fall in the same cluster. Hence customer segmentation is another important aspect for a growing business. You can segment a customer based on his/her purchase history, vintage, timestamp, frequency, momentary etc. and then isolate each segment to build personalised messages, discounts or vouchers.
Implementation: Suppose you run an apparel stores and a festival season is coming up. All the brands in your stores are asking you achieve an ambitious sales target for which they are willing to operate at a higher burn rate. Now all you need to do is communicate the offers and discounts for all the brands to all your customers. You calculated that if you send all the brands offers to all the customers you will run through your monthly marketing budget in an hour. Hence you segment the customers based on their purchase history and attributes and start sending personalised messages. When you do this you are saving a lot of the marketing cost while increasing the probability of the customers to respond to the discounts and offers there by giving you sales.
Algorithms: Distribution based, Density based and centroid based algorithms are the most popular ones for this model. While doing this project we would also go through some market standard approaches like RFM, cohorts etc. to flag the cluster to which a user belongs to.
Make an informed decision.
Data science is a field of computer science where we deal with data gathering, analyzing and solving a problem with the help of data and algorithms.
Gathering, analysing and solving a problem with the help of data and algorithms is the crude definition of data science. Just like any other branch of science, Data Science heavily consists of experiments, research and ground breaking findings. It is the intersection between Mathematics & Computer Science and given the IT boom the application of data science in today’s tech heavy world is endless. You can be sure that some of the major challenging problems in e-commerce, pharmaceuticals, banking, logistics, medicine, astrophysics etc. are being solved with the help of data science.
Turning award winner Jim Gray famously described Data Science as the "fourth paradigm of Science" with empirical, theoretical and computational being the first three.
That being said, now you may ask "What do you call the practitioners of this branch of science?" Well, just like the practitioners of physics are called physicists the practitioner of data science are called data scientists. A Data scientist is the person who asks the right question and have the right tools to solve a complex problem with the help of data. A data scientist assumes many roles in the course of solving a problem and toggles between those roles effortlessly. Given the application, challenge, high pay and demand there is no doubt that Data scientist has been coined as the sexiest job of 21st century.
A Data Scientist is a qualitative thinker and have mastered at least one programming language for crunching the data and knows one or more languages to mine the data out of the system. A data scientist’s core skill is his analytical thinking and the way he approaches a problem. He could break a complex problem in smaller chunks and can look at a single problem while shifting the paradigm so that no aspect of the problem remains unturned or untouched. And finally, a data scientist is skilled in devising strategies from the gathered observation while justifying those strategies with data and communicate them in ways that can be easily digested. Just like a great Storyteller the data scientist projects his work like a movie which is engaging at multiple levels and delivers a message which is understood and applied by the stake holder.
The fact that 90% of all the data available today was generated in the past 3 years vouch for the insight that human resources needed to handle and analyze the data is only going to grow in the coming decade. The cost of storing the data and cost of computing & crunching the data has fallen dramatically there by empowering even SMEs along with giant MNCs to get insights with help of data scientists from the massive data warehouses which were previously untouched.
Approximately 1 Lakh analytics jobs are stilled required to be filled in India which is 45% more than last year (2019). The fact that India only contributes to 6% of the Data Science job openings worldwide & the growth of the demand of data scientists worldwide was higher than India tells us that Indian market is just warming up to get into the world wide data science race. Further, companies like Accenture, Amazon, KPMG, Honeywell, Wells Fargo, Ernst & Young, Hexaware Technologies, Dell International, eClerx Services & Deloitte including their offices in India were the leading organisation with most number of Data science and analytics opening. To all the above mentioned facts if we add the insights that an analytics position on an average remains open for only 45 days we can easily infer that the world is increasingly demanding more data muscle to push itself in this age of information.
Banking and Financial services are the top Leader in the worldwide industry to apply data science in their business and other industries like energy & utilities, Pharama & Health care, E-commerce, Media & entertainment, Retail & CPG, Automobile, Telecom, Travel & hospitality etc. are still in their nascent stages when it comes to taking data backed decisions. As we enter this new decade we would definitely see how these industries would leverage data science to take data backed decisions. When they inevitably do so, they would need an army of data scientists.
The salary trends and the range of salaries for Data scientist in the opening quarter of 2020 are given below:
|Company||Average Salary||Salary Range|
|Microsoft||17,24,396||13,00,000 to 21,00,000|
|IBM||13,73,956||5,59,000 to 35,86,000|
|Cognizant||10,62,725||4,26,000 to 16,55,000|
|Accenture||10,87,378||4,39,000 to 23,31,000|
|Fractal Analytics||14,17,477||9,00,000 to 18,86,000|
|United Health||11,75,049||6,83,000 to 16,15,000|
|Ericsson Worldwide||13,37,640||3,71,000 to 33,44,000|
|Fuzzy Logix||14,42,152||5,17,000 to 20,91,000|
Now that you’ve a good understanding of the salary and job trends in Data science field let us introduce you the problems you might solve when you choose this domain. We listed down few problems on which elite data scientists are working on:
- Cancer and other disease detection
- Character recognition
- Increasing effectiveness of logistics
- Crime and terror detection
- Self-driving cars
- Impact of climate change on various aspects of earth
- Human like Chatbots
- Traffics time series forecasting
- Fake news detection
- Forest fie prediction
- Human face/writing/age/gender detection
The above mentioned complex problems requires the best brains to ponder over the data bases and when you choose data science you become the part of the group who wants to solve real world problems and touch billions of life’s meaningfully.
Although a certification adds value to your resume but some good capstone projects and an in-depth knowledge of the subject trumps any certification. So it would be fair to say that having certification is not a game changer if you can’t back that up with a knowledge and hands on experience. A recruiter would rather look for projects and experience to assess your skills as a data scientist and not for a certificate whose credibility on the rigorousness and content are questionable. It just does not make any sense for a recruiter to blindly believe that a certification can bypass knowledge that need to weigh before coming to a conclusion.
If you are a fresher an in depth knowledge of the projects that you have put in your resume and the link to your GitHub account should suffice the need, and if you’re an experience personal then in addition to aforementioned requirements you would be assessed on your domain and industry experience. If you are comfortable with your projects and have confidence in your skill set you would always have a competitive advantage over any certificate holder.
That being said, at Programink we do provide a ‘certificate of completion’ which reflects the fact that you have actively completed a rigorous course in data science. The Programink certificate of completion is only given to the students who have completed the entire course and have excelled in the programs quizzes, assignments and projects. What that means is even if the certificate slips through recruiters’ eye the wide portfolio of projects that you would have added in your resume and all the knowledge you would have gained while working on the assignments while completing the course will surly put you in the lime light.
The answer to the above question is subjective to your profile and willingness to learn.
For fresher’s, even if you start today you would need 3-4 months of dedicated learning to land an entry level job as Junior/Associate Data Scientist. If you put in more hours to gain the domain and industry experience you could even convert a Data scientist role opportunity directly. But for fresher it generally tougher because this position always demands an extraordinary combination of computer science, math, industry exposure, domain knowledge and corporate experience. All of these, for a fresher, is tough to bring on to the table for obvious reasons. But nevertheless, if you believe in yourself and are willing to put the desired work against your commitments then you will excel and would defiantly bypass all the social norms and at Programink we commit to walk this difficult path along with you so we can help you reach your career goals.
For an experienced personal it is comparatively easier as out of the required prerequisite the experience of the personal brings in the domain knowledge, industry exposure and the corporate experience by default. Rest of the skills like mathematics, computer science and hands on projects could be gained over 4-5 months of rigorous training in the subject while working on some complex problems which would help reshape your profile. Even if you’re experienced in domains which does not directly translates to data science or information technology we at programink guarantee you to guide you in path which would help you make this shift in career a cake walk. So sum things up for an experienced professional, apart from the evident advantages you would get from your work experience if you could add the required Mathematics and projects to your arsenal than you would be ready to enter the race.
Now as we have said that the years and work required to become a data scientist is subjected to many criteria’s so we would urge you take the free career counseling we provide at programink. After understanding your current skills and experience we would be able to chalk out a personalized career approach for you which would be realistic and achievable. Please check out the contact section to get touch with us.
Programink adheres to the philosophy that ‘Learning should never be a financial burden for the students’. This philosophy is deeply hardwired in us and has helped us designed courses and facilities which ease the financial burden on students’ shoulders.
First approach is for all the courses above 20,000 you can avail a study loan of up to 80% with minimal rate of interest. That takes off any additional pressure of finances that students might had to face otherwise. You can now easily invest in your learning with balancing your monetary commitments.
Another approach adhering the philosophy was to bundle the courses in smaller chunks and priced them accordingly. This will help the students to take up the learning journey without shelling out a huge lump of amount.
Then we have tie ups with many MNCs and SMEs who want to shell out money to anyone who can solve some of their data related problems. So if the students can take up the challenge and deliver the satisfactory results then programink would credit the students with majority of the quoted amount. Further we also provide internship opportunities which would help a students to earn while they learn.
Data science training process
The instructors programink associates with are industry experts with relevant work experience, have a passion for teaching and have great communication skills. That’s the basic bars we’ve set so that we could deliver personalised market standard content with high efficiency. To know more about our instructors please check our ‘About our instructors’ section.
Yes, at Programink, we have dedicated mentors to attend to each student’s doubts and queries. Whether it is counseling for the career or clearing queries related to the previous data science classes, we at programink always have dedicated mentors who would respond with precise answer in the shortest amount of time.
Talking about the content, delivery, timing and knowledge, there is absolutely no difference. What we teach in our offline classes is replicated with no loss in our online classes as well. We have infrastructures which make our online classes smooth and highly efficient. Students being online can raise their hands and ask doubts during the classes and the instructor will clarify the doubts in real time. To make surely that the online experience never breaks we have built or have fully purchased IT infrastructure which has a dedicated team monitoring its stability 24x7. At Programink, we understand that the importance of the classroom training but keeping in mind that most of our students are working professionals we have made sure that we have world class online platforms and have designed our content in a way which is online friendly. To see this difference yourself we urge you take up the free demo session that we offer and experience online sessions.
We always try to keep our self-updated when it comes to technology. To mention some of the essential elements of our lab set up, we have:
- Python version 3
- Jupyter notebook
- Advance Excel
We broadcast our classes via our virtual learning platform, making it less likely to miss. But even if you do, you are most welcome to retake it in another batch. We also has recorded sessions our classes so that we could share them with students who has missed a class for genuine reason.
Data science certificate and projects
At programink we committed to deliver the holistic content of a subject we teach while matching the latest market trends and standards. When it comes to data science we sure are on our toes to provide our students with the updated content so that our students never become the lagers in this field.
The tools for data science can be broken down into three parts, namely: 1) Data mining tools 2) Data analysis tools 3) Data visualization tools. Let’s discuss all these parts in details.
1) Data mining tools: These are the tools you would need to fetch the data from the database. The most common data mining tools for structured and RDBMS are SQL, PostgresSQL, AWS Redshift. Although the names are different but the syntax of these tools are not much different. So it would be safe to say that if you mastered any one of them then then you can migrate to the other mentioned tools with little to no effort. For Non-relational database MongoDB(NoSQL) is what most of the firms relies on. Apart from the syntax the fundamental difference between SQL and MongoDB is that MongoDB supports JSON structure of files which makes it easy for firm to scale up the when data volume is growing exponentially. Apart from the traditional databases sometimes we also need to gather/mine the data from web, csv or Jason files. These type of data could be mined via web scrapping and python pandas library. At programink we make sure that our students are comfortable with both relational and non-relational database and have skills to gather the data from other required sources. Hence, we have designed our curriculum which would help you gather skills to mine the data no matter where it sits.
2) Data analysis tools: After mining the data the real work of a data scientist beings as he starts to analyze the data. The steps involved in the process of analyzing is dynamic as it changes with the type of and what analysis you want to perform. Tools like Python, R and Excel are some open source and easily available software which covers almost all the requirements demanded by a data scientist to analyze the data. Python and R being the most trending tools we at programink have included an in depth dive and hands on experience for each of these tools so that our students has these weapons in their arsenal. Talking about python, as it is right now the hottest language to learn data science, we have made sure that our courses included Pandas, Numpy, Matplotlib, Statsmodels, Scikit-learn, seaborn etc. and as python community release upgraded and new packages we make sure that we update our course accordingly. The same is true for R programming languages and all the important packages like dplyr, purr, sqldf, rshiny, rmarkdown, flexdashboard etc. are covered in the most extensive way.
3) Data visualization tools: What good is a knowledge which cannot be communicated in an efficient and simple way, we as a data scientist need to learn how to present our insights and findings on the platter so that it can easily digested by our stakeholders. Hence, at Programink we put equal effort in making our student combination flawless. Packages like Matplotlib and Seaborn which extensively used in python to visualize the data would be covered in detailed manner with hands on projects and assignments. Similarly, for R we deep dive into packages like ggplot to uncover the art of visualizing the data.
Absolutely. We encourage our students to take up an individual project each and our training mentors are available round the clock for all the support you many need. So, if you want to bring in some real world problems from your current industry programink would be honored to guide you in devising the approach to solve the challenge.
The proof that programink really cares about your personal data science projects could be found in our data science curriculum where we have dedicated a section to discuss the personal projects brought in by our aspiring data science students.
If you would have gone through our course structure then you would have noticed that we have projects which covers multiple domains and industry at different levels of the course.
Let’s talk about the capstone projects that we offer in our course. We have chosen the following industry in which we would be providing the capstone projects:
- Demand Forecasting and improving the accuracy of the forecast
- Increase production efficiencies to lower costs
- Increase product quality
- Recommender Model to recommend products to a customer
- Personalising marketing for cluster of users
- Data backed cross selling and up selling
- Customer behaviour analysis
- CLV prediction
- Optimizing Employee retention
- EDAs on KPIs for employees
- Optimizing budgets for HR verticals
- Optimizing the price
- Recommender model
- Seller and customer fraud detection
- Improving customer service by automating it
- Market basket analysis
- Risk analysis model
- Fraud detection model
- Customer clustering
- Credit risk analysis
- Finding patients with rare diseases
- Building patient treatment journey
- Detecting physicians availability from database
Although the projects are subjected to change with the growing market we are only going to add more industries in the near future.
Out of the above mentioned industry students can chose any two and those would be covered in the classroom. Apart from the chosen two projects one project would be requested to students to solve independently and that project would be graded. For the rest of the projects the data sets and business case would be provided and the students would be encouraged to finish them off by themselves so that they could add these projects in their portfolio. Programink would provide 100% assistance in solving those projects but a student would be expected to complete them in the given time frame.
It’s hard to put a number on lab session hours as they are subjected to the number of projects and pace of the students. At programink we have no cap on the number of hours for lab session you can come in practice for any number of hours when you’re enrolled.
Programink believes that networking is a crucial part any professional career, so we put in constant efforts to brings in people from relevant domains and with extensive experience so that talents, skills and ideas can cross pollinate. With that ideology our answer to the question is, yes we do conduct hackathons where we put up real word challenges and give out exciting prizes. We also conduct meet ups with solution centric objectives which are attended not only by our students but also industry experts. Do check out our newsletter, blog or events section to find out our upcoming events arranged by programink.
Data science jobs in Bangalore
Data scientist, Business Analyst, Data Analyst etc. First let’s try to understand the roles in Analytics domain.
1. Data Analyst : Converges data to facilitate analysis by answering descriptive questions. Example: What is/was the trend in sales of product X.
2. Business Analyst : Provides data backed insights and formulates strategies for business by answering prescriptive questions. Example: What can be the strategy to boost sales of product X.
3. Data Scientist : Ties up business knowledge with statistical inferences to answers predictive questions. Example: What will be the change in sales of product X given strategy Y.
At programink we try to cover all the opportunities available in the data science domain step by step and try to conclud each level with hands on case study so that the students are clear on what are their strengths and weaknesses so that later when seek career council they themselves have a better understanding of their capabilities.
Programink has corporate placement tie-ups with both fortune 500 and startups. So when have completed the course you would already know what companies are the leaders in this domain as you would have already started to receive calls from them.
Some of the top companies which were leading the data science field in 2019 are as follows:
- BRIDGEi2i Analytics Solutions
- Cartesian Consulting
- Envestnet Yodlee
- Publicis Sapient
- Tredence Inc.
- ZS Associates
- Fractal Analytics
- Affine Analytics
- EXL analytics
Yes. We have a very refined and up-to-date data science interview questions and answers series for job seekers in all categories. We’ll also conduct mock interview and practice interview case studies and prepare out students with all the possible questions that are asked in the data science interview.
Practically unlimited calls within 6 months of course completion until you get placed. We also guarantee placements money back.
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Course Name: Full-Stack Data Science [with Placement Guarantee Program]
Programink as a data science training institute in Bangalore is committed to provided the highest level of data science training. Below is a mention of how you will gain step-by-step from our data science course.
Elective 1: Analytical thinking and Data Science fundamentals
We start by building the thought process we would need throughout this course and subsequently in the data science career, the elective then dives into basic tools and techniques that are used in the market. The first project that would complete in this elective would give a sense of what this field is all about as we expose you to some real world challenges and data. This elective ends with end to end project with a presentation from your side as a wrap up.
Elective 2: Python for Data Science
This where you would be introduced to python programming language and its use and importance in the data science domain. Even if you have no prior knowledge of python programming language we make sure that you become proficient in it so that you can play around and work on data sets effortlessly in the next elective. This elective will cover advance numpy and pandas library along with techniques to handle and shape the data.
Elective 3: Data pre-processing and visualization
Now that you have good grasp of python we will introduce you data wrangling techniques and visualization. This would be fun because we will dong it in parallel with solving the business cases so while learning you would also be working on project which would be a base project to build on the final case study for this elective.
Elective 4: Advance Statistics
This is where Data Science gets better with addition of advance statistical concepts. Nowing the numbers and there inferences makes you visualize the model and its implications.
Elective 5: Machine learning algorithms
Here is where the real fun begins, this elective kick starts with a project which will be small revision of few concepts form the previous electives. Then slowly we take you through the concepts of different machine learning techniques and its application. By the end this elective you’ll not only be identifying the types of algorithm you would use to solve a particular problem but also know how to quantify and improve your predictions and classifications. You’ll have all the knowledge at your disposal to approach and tackle different types of data science problems.
Elective 6: Capstone project
Last but not the least the is a full fleged indistrial project that you will do under the able guide of an industry expert and your project mentor. The data sets will be all new and so will the problem statement. You can proudly mention this project as an achievement in your career.
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