Introduction to Data Science Certification Training
ExcelR offers 160 hours classroom training on Business Analytics / Data Scientist / Data Analytics. We are considered as one of the best training institutes on Business Analytics in Gurgaon. “Faculty and vast course agenda is our differentiator”. The training is conducted by alumni of premier institutions such as IIT & ISB who has extensive experience in the arena of analytics. They are considered to be one of the best trainers in the industry. The topics covered as part of this Data Scientist Certification program is on par with most of the Master of Science in Analytics (MS in Business Analytics / MS in Data Analytics) programs across the top-notch universities of the globe.
Our Business Analytics certification training course is designed by the industry experts, which is precisely tailored for the professionals who wants to pursue a career as a Data Scientist in job market. We offer a comprehensive placement program where we equip you with hands on training on Business Analytics, resume preparation, case studies, Live projects, mock interviews etc. We do the necessary hand holding till the participants are placed in a job in the field of Analytics.
What is Business Analytics /Data Analytics/ Data Science?
Business Analytics or Data Analytics or Data Science certification course is an extremely high-in-demand profession which requires a professional to possess sound knowledge of analysing data in all dimensions and uncover the unseen truth coupled with the logic and domain knowledge to impact the top-line (increase business) and bottom-line (increase revenue).ExcelR’s Data Science curriculum is meticulously designed and delivered matching the industry needs and considered to be the best in the industry
Also, Google Trends shows the upward trajectory with an exponential increase in volume of searches like never seen before. This is proof enough to back the statements made by Harvard Business Review and the business research giants, that Business Analytics will be the most sort after professional world has ever witnessed.
What is a Data Scientist? Or Rather Who is a Data Scientist?
Data for a Data Scientist is what Oxygen is to Human Beings. This is also a profession where statistical adroit works on data – incepting from Data Collection to Data Cleansing to Data Mining to Statistical Analysis and right through Forecasting, Predictive modelling and finally Data Optimization. A Data Scientist does not provide a solution; they provide most optimized solution out of the many available.
Gartner predicted in 2012 that Data Scientist & Business Analytics jobs will increase to the tunes of Millions by the end of 2015. This is very evident with the rise in job opportunities in various job portals. As a Data Scientist or an aspirant, you should not believe us. Go! research for your own and confirm the facts and figures.
Who should do Business Analytics Course
Professionals who can consider Business Analytics / Data Analytics Certification/ Data Science certificate program Training as a next logical move to enhance in their careers includes:
Professionals working on Business intelligence & reporting tools
Professionals working on Data warehouse technologies
Statisticians, Economists, Mathematicians
Software programmers (they have an edge in writing code to accomplish a prediction/classification/forecasting model)
Business analysts (they have an edge in terms of industry/sector/domain experience)
Six Sigma consultants who already have exposure to statistics
Freshers (market demand is thriving organisations to hire the freshers trained on analytics)
How to become a Data Scientist?
Accrue knowledge on dealing with data by getting trained and/or certified by any of the well-known institutes which have rich experience working closing with the ever-evolving industry.
Knowing data analytics tools or data mining software alone will not help you analyze data.
So WHAT else is required?
One should possess Domain/Sector/Industry knowledge and learn relevant concepts to strike the right nail. A few such examples which are not limited to those mentioned are:
One into web development might want to learn Web Analytics
One into search engine optimisation might want to learn Social Media Analytics & Website Analytics
One into sales & marketing might want to learn Marketing Analytics, Customer analytics, Twitter Analytics, Facebook Analytics, Social Media Analytics, Data Collection tools
One into human resources might want to learn Workforce Analytics
One into health care might want to learn Healthcare Data Analytics
Real world problems
Life Sciences & Health Care – Wearable Devices
Many people across the globe are wondering on how to predict diseases very early so that changing lifestyle habits will cure the disease instead of medication. For this people started wearing health bands, a few call them sports band which is used to track heart beat rate, calories burnt, sleeping patterns, number of steps taken (walked) and many more. Jawbone is the most famous wrist band and its users have helped generate about 130 million nights of sleep and experts call it the biggest sleep study on the planet. Also recorded are about 1.6 trillion steps and 180 million items of food.
We can tag the data to our personal doctor who will monitor and inform us on what diseases we are likely to be infected with and what precautions should be taken to avoid it. Sounds WOW!
Retail – Location Based Analytics
Thousands of footfalls are witnessed in any known shopping mall across the globe. Can the store owners within the mall convert the footfalls into revenue? Answer is ‘YES’! The moment a person connects to free wi-fi available in the mall, a unique MAC address is assigned to the person. From there on the details such as time spent in a store, speed of movement (moving across the wifi zones/range), past buying behaviour, number of times a person visited a store versus number of times purchase happened and various other parameters are gauged, to send a personalised coupon which will lure the potential customer to become a source of revenue.
Number of coupons sent versus number of purchases & Number of coupons sent versus amount purchased are the key Business metrics captured & evaluated to enhance the prediction model. Amazing, isn’t it!
Watch out this space for interesting use cases…..
Why one should pursue a career in Data Science/Business Analytics/Data Analytics:
One may question if Data Science certification is worth it? The answer is yes. Data Science/Analytics is creating myriad jobs in all the domains across the globe. Business organizations realised the value of analysing the historical data in order to take informed decisions and improve their business. Digitalization in all the walks of the business is helping them to generate the data and enabling the analysis of the data. This is helping to create myriad Data Science/Analytics job opportunities in this space. The void between the demand and supply for the Data Scientists is huge and hence the salaries pertaining to Data Science are sky high and considered to be the best in the industry. A Data Scientist career path is long and lucrative as the generation of online data is perpetual and growing in the future. ExcelR offers the best Data Science online certification training along with classroom and e-learning certification courses. The complete Data Science course details can be found in our course agenda on this page.
Data Scientist Training and Placement:
Subsequent to the completion of our Data Science Certification program, assignments and projects placement assistance will kick start. Help will be rendered in terms of resume building, FAQs for the interviews, one-to-one discussion on job description during interview calls, etc. A couple of mock interviews (one on one / telephonic) will be conducted by the SMEs to evaluate the grey areas and areas of strength. This helps the participant to retrospect and understand their interview readiness. Participant can attend and successfully crack the interviews with complete confidence. ExcelR offers the best Data Science training and the reviews from our past participants vouch our statement.
Data Science Course Introduction Video
Data Science Introduction
Watch our sample e-learning video recorded by industry’s best trainers with extensive subject knowledge expertise and who are considered to be the best trainers of the industry. All the participants will be provided access to our state-of-the-art Learning management system (LMS) at ExcelR.net, where one can access end to end course videos at your own pace & convenience sitting back at your home. Videos can be accessed from your desktop, mobile, tablet, etc. Switch back and forth as you choose
Things you will learn……
Upcoming Batches for Certified Data Scientist Training Program
Data analytics is a profession which caught the attention of the world only since 2 years. Because of this very reason most companies are struggling to close the demand-supply gap. Hence, people who are trained and have decent exposure towards the data analytics techniques are recruited immediately.
There are a lot of job opportunities in various job portals for freshers. The key thing employer would be keen to know is whether you have the conceptual knowledge or not. The projects provided by ExcelR in various concepts will only reinforce your learning to make you market ready for the jobs.
Yes and No. Yes in the sense programming skills would be required & No in the sense one need not have extremely strong programming skills. However, we at ExcelR ensure that you get sufficient exposure on the statistical programming tool called ‘R’. We start right from the basics assuming you do not have any exposure towards programming.
R has approximately 50% market share & it is open source (free of cost). Hence, R is very lucrative in the analytics space. Almost all the jobs are asking for experience & exposure in R. Demand for other statistical tools is decreasing steadily & hence it is recommended to be futuristic and invest time in learning R.
Salaries range varies based on experience, industry, domain, geography & various other parameters. However, as a general thumb rule, we can apply the following formula:
Salary = No. of years of experience * 3 Lacs per annum (India – INR)
Salary = No. of years of experience * $ 1200 to $1500 per annum (Overseas – USD)
Business Analytics Course Agenda
1. Basic Statistics
—• Data types and its measures —• Random Variables, its applications with exercises —• Probability – Applications with examples —• Probability distribution with examples —• Sampling Funnel – why and how —• Measures of central tendency — o Mean, Median, Mode —• Measures of dispersion — o Variance, Standard Deviation, Range – Its derivation —• Measures of Skewness & Kurtosis – Graphical representation and application
—• Various graphical representation of data for analysis
—o Bar Chart —o Histogram —o Box Plot —o Scatter Plot —• Continuous Probability distribution —o Standard Normal distribution / Z distribution —o F – distribution —o Students T distribution —o Chi Square distribution —• Discrete probability distribution —o Binomial distribution —o Negative Binomial distribution —o Poisson distribution —• Computing probability from Normal Distribution —• Building Normal Q-Q plots & its interpretation —• Central Limit Theorem for sampling variations —• Confidence interval – Computation and analysis
2. Hypothesis Testing -What and How
—• Formulating a hypothesis statement —• Parametric tests —o 1 sample, 2 sample t test —o 1 sample Z test —o 1 Proportion, 2 Proportion test —o Paired t test —o One way ANOVA —o Chi – Square test —• Nonparametric Tests —o 1 sample Sign test —o Mann – Whitney test —o Kruskal – Wallis Test —o Mood’s Median test
3. Regression Analysis
—• Measure of correlation coefficient and it analysis —• Regression model using “Ordinary Least Squares” —• Coefficient of determination as a strength of a model —• Prediction interval and Confidence interval —• Prerequisites to Regression —o Linearity —o Independent —o Normally distributed —o Equal variance —• Regression techniques —o Linear Regression — Simple — Multiple —o Logistic Regression — Simple — Multiple —• Model building using regression —• Measures of accuracy —• Model improvement techniques —• Analysis of regression output with case studies —• Imputation Techniques —o Listwise, Pairwise Deletion —o Mean/Mode Substitution —o Regression Imputation —o Hot Deck, KNN Imputation
4. Data Mining / Machine Learning
—• Supervised vs Unsupervised —• Basic Matrix Algebra —• Data Mining Unsupervised —o Clustering – its applications and limitation — Hierarchal — Non Hieratical (K-Means) —o Affinity Analysis / Association Rules — Measures of association — Support, Confidence, Lift Ratio — Sequential pattern mining —o Recommender Systems — Methods and tricks of the trade —o Dimension Reduction Techniques — Principle Component Analysis — Singular Value Decomposition —• Data Mining – Supervised —o Black Box demystified — Neural Networks — Support Vector Machines —o Classification / Pattern mining strong>— K Nearest Neighbor — Naive Bayes — Decision Tree & Random Forest — Decision Tree C 5.0
5. Text Mining & Natural Language Processing
—• Text extraction from webpage —• Word clouds – analysis with context —• Negative and positive words —• NLP —o Latent Dirichlet Allocation (LDA) —o Structured Extraction —o Emotion Mining
—• Strategy for Forecasting —• Analysis by Graphical Representation —• Components in a time series data —• Plots of Time series data —• Autocorrelation function / Correlogram —• Visualizations – How to perform —• Methods of Forecast —o Naïve methods — Simple and Moving Average —o Model driven —o Regression Model – Linear, —o Exponential, Quadratic —o Econometric models —o Seasonality factored model —o Autoregressive model —o Random Walk —o Data Driven — Smoothing — Exponential Smoothing — Advanced Exponential Smoothing — i. Holt’s Method — ii. Winter Method — AR, MA, ARIMA models —• Analysis of errors in forecast —o Skewness of Error —o Types of error measure — Mean Error (ME) — Mean Absolute Deviation (MAD) — Mean Squared Error (MSE) — Root Mean Squared Error (RMSE) — Mean Percentage Error (MPE) — Mean Absolute Percentage Error (MAPE)
7. Data Visualization
—• 3 important principles of Visualization —• Lie Factor —• Using consistent scales —• Presenting data in the context —• Data-ink ratio —• Tufte’s Graphical Integrity Rules —• Tufte’s Principles for Analytical Design —• Various chart junks & how to avoid chart junks —• Dashboards –Good, Bad & Ugly —• Affordance Theory
—• Introduction to the various file types —• How to access help —• Quick introduction to the user interface in Tableau —• How to connect to the data sources —• How to join the various data sources —• How to create data visualization using Tableau feature “Show Me” —• Reorder & remove visualization fields —• How to sort & filter data —• How to create a calculated field —• How to perform operations using cross-tab —• Working with workbook data & worksheets —• How to create a packaged workbook —• Creating various charts —• Creating maps & setting map options —• Creating dashboards & working with Dashboard
9. R & R Studio
—• Introduction to R —• Working with Packages —• Performing various regression and data mining techniques using R Studio
—• Introduction to NodeXL and its application in Network Analysis
—• Using XLMiner for performing various forecasting techniques
—• Performing various regression and data mining Techniques using Python
Providing the solution is not the objective of a data/business analyst. Providing the most optimal solution out of
multiple solutions that is exist by considering all the constraints is the key for success.