Undoubtedly one of the best Business Analytics courses in India and globally. Apt for candidates who want to start from basic Business Analytics tools like Excel, SQL, Tableau and graduate to advanced tools like R, Python for Data Science and Machine Learning. AnalytixLabs pioneers in Business Analytics training and launched first version of Analytics Course in Delhi NCR in 2011!
However, learning tools without techniques is half the job done in today's Analytics world. This Business Analytics course in India encompasses basic statistical concepts to advanced analytics and predictive modelling techniques, along with machine learning. You will learn all the skills required for a promising career as Business Analyst and solve real world business problems. This certificate course will help you to develop comprehensive data science skills pertaining to data visualization descriptive and predictive analytics for driving smart business decisions.
Evolved from our most popular Business Analytics training, this is the best course in India curated for candidates who are looking for job oriented business analytics certification but have no prior knowledge of any business intelligence or data analytics tools. Most extensive business analytics training in Bangalore, Gurgaon, Noida and with flexibility of also attending the live online training and e-learning mode as well.
This analytics certification course is for all those aspirants who want to switch into the field of data science and begin their career as business analyst. With aim to provide all the aspirants world class Data Science and Business Analytics skills irrespective of their location. You have flexibility to attend this Business Analytics online course through fully interactive live and e-learning mode as well with extensive doubts support.
Classroom and Bootcamp options are available for candidates looking for Business Analytics course in Bangalore, Noida and Gurgaon. We are proud to share that this program is also rated among top certifications by presitigous publications like AIM & Higher Education review.
Business Analytics training duration: 450 hours (At least 150 hours live training + 48 hours video based module + around 10 hrs of weekly self-study and practice)
Who Should do this course?
Beginner candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Business Management who are looking for Business Analytics training to start their career in the field of Analytics and Data Science.
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|Course Duration||450 hours|
|Tools||Excel, VBA, Tableau, SQL,R, Python|
|Learning Mode||Live/Video Based|
|Next Batch||14th June, 2020 (Noida)|
21st June, 2020 (Gurgaon)
Time Series Forecasting: Solving forecasting problems
Machine Learning -Predictive Modeling – Basics
•Introduction to Machine Learning & Predictive Modeling
•Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting
•Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
•Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
•Overfitting (Bias-Variance Trade off) & Performance Metrics
•Feature engineering & dimension reduction
•Concept of optimization & cost function
•Overview of gradient descent algorithm
Machine Learning -Predictive Modeling – Basics
•Overview of Cross validation(Bootstrapping, K-Fold validation etc)
•Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )
Segmentation: Solving segmentation problems
•Introduction to Segmentation & Role of ML
•Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
•Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
•Behavioral Segmentation Techniques (K-Means Cluster Analysis)
•Cluster evaluation and profiling - Identify cluster characteristics
•Interpretation of results - Implementation on new data
Unsupervised Learning: Segmentation
•Concept of Distance and related math background
•Spectral Clustering (DBSCAN)
•Principle component Analysis (PCA)
Supervised Learning: Decision Trees
•Decision Trees - Introduction - Applications
•Types of Decision Tree Algorithms
•Construction of Decision Trees through Simplified Examples; Choosing the "Best" attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
•Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
•Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
•Decision Trees - Validation
•Overfitting - Best Practices to avoid
Supervised Learning: Ensemble Learning
•Concept of Ensembling
•Manual Ensembling Vs. Automated Ensembling
•Methods of Ensembling (Stacking, Mixture of Experts)
•Bagging (Logic, Practical Applications)
•Random forest (Logic, Practical Applications)
•Boosting (Logic, Practical Applications)
•Gradient Boosting Machines (GBM)
Supervised Learning: Artificial Neural Networks (ANN)
•Motivation for Neural Networks and Its Applications
•Perceptron and Single Layer Neural Network, and Hand Calculations
•Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
•Neural Networks for Regression
•Neural Networks for Classification
•Interpretation of Outputs and Fine tune the models with hyper parameters
•Validating ANN models
Supervised Learning: Support Vector Machines
•Motivation for Support Vector Machine & Applications
•Support Vector Regression
•Support vector classifier (Linear & Non-Linear)
Supervised Learning: Support Vector Machines
•Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
•Interpretation of Outputs and Fine tune the models with hyper parameters
•Validating SVM models
Supervised Learning: KNN
•What is KNN & Applications?
•KNN for missing treatment
•KNN For solving regression problems
•KNN for solving classification problems
•Validating KNN model
•Model fine tuning with hyper parameters
Supervised Learning: Naïve Bayes
•Concept of Conditional Probability
•Bayes Theorem and Its Applications
•Naïve Bayes for classification
•Applications of Naïve Bayes in Classifications
Text Mining & Analytics
•Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
Text Mining & Analytics
•Finding patterns in text: text mining, text as a graph
•Natural Language processing (NLP)
•Text Analytics – Sentiment Analysis using R
•Text Analytics – Word cloud analysis using R
•Text Analytics - Segmentation using K-Means/Hierarchical Clustering
•Text Analytics - Classification (Spam/Not spam)
•Applications of Social Media Analytics
•Metrics(Measures Actions) in social media analytics
•Examples & Actionable Insights using Social Media Analytics
•Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)
•Fine tuning the models using Hyper parameters, grid search, piping etc.
Project - Consolidate Learnings:
Applying different algorithms to solve the business problems and bench mark the results
Stationary Time Series Methods
Trend Based Time Series
Seasonal Time Series
With rise of digitization, organizations are flooded with ever increasing flow of information. A business analyst or a data analyst uses a combination of skills to derive actionable insights which can be vital for making smart decisions. And in this scenario, businesses rely upon Data Analysis skills more than ever before to stay competitive. This is why business analytics demand has surged globally, creating a huge demand for business analysts. Analytics Industry in India is expected to grow 7 times in next 7 years (from 2019 to 2025), as per the finding of ‘Analytics Industry Study 2018’ conducted by AIM & AnalytixLabs. So, like all previous years the demand of Business Analyst will stay strong with promising opportunities across various domains.
If we go with trends suggest by LinkedIn and Glassdoor some of the top organizations hiring for business analytics in India are Tata Consultancy Services (TCS), Accenture, Cognizant, Deloitte, American Express, Evalueserve, Ernst & Young, Amazon and Flipkart.
In terms of salary packages, Business Analysts are some of the highest paid professionals as compared to their peers. As per Glassdoor, the average beginner level salary for a business analyst in India is INR 6,44,857 per annum. However, post some experience analysts earn up to INR 12,00,000 per year, especially those who are adept at the latest techniques and tools. With higher experience and well established domain expertise, salaries can range anywhere between INR 25,00,000 to INR 40,00,000 annually.
No, coding skills are not mandatory for Business Analytics. Most of the analysis can be performed using the tools like Excel, Tableau, Power BI without coding, whereas while working on SQL one needs basic understanding of SQL commands. However, to leverage advanced analytics it is helpful to have working knowledge of specialized tools, like R and Python. But even in this case with right coaching, candidates with no-prior technical background can work effectively on these tools.
For most of the aspirants a professional certification can effectively help in up-skilling and acquiring job relevant skills in usually around 6 months (with part-time training). Apart from this there are several post-graduate programs available, with duration of 1 or 2 years and average investment ranging from 6-12 lakhs. In short to medium term, part-time certification offer better ROI, but this may vary based on candidate’s overall profile and career stage.
In best case scenario, an undergraduate degree in quantitative or technical stream, possible followed by an MBA and then a certified course in BA would ensure your access to the upper echelons of BA jobs. Going with an institute of certain repute is one of the best options, but candidates should consider some key aspects:
• Domain expertise: Given the popularity, plethora of institutes have jumped into this foray. But only handful of institutes bring domain expertise, deep intellectual property and significant experience to deliver industry relevant program.
• Industry recognition: A top institute would have strong industry presence in the particular domain, with prestigious clientele. This certainly adds meaningful value to the certificate and industry recognition.
• Mentors’ experience: It is utmost important that faculty members/ mentors have vast industry experience and also strong hands-on themselves.
• Industry relevant curriculum: Curriculum should cover latest tools and techniques with enough practical components. Frequent curriculum upgradation is key in such a fast evolving field.
• Projects and case studies: Projects and assignments should have extensive coverage of real world problems from key industries, so that it inculcates confidence in candidates to face interviews and on job challenges.
• Post class support: It is imperative that candidates have ample support available outside class hours for doubts and problems they face while self-studying and working on projects. Institutes with only part time instructors usually lack in this important aspect.
Among numerous institutes, AnalytixLabs is certainly one of the finest and well renowned option. AnalytixLabs has been a pioneer in Data Science learning since 2011 and is constantly rated among India’s top institute with repeated clientele of several prestigious MNCs. Considering the value for money, industry relevant programs and stellar track record no one can overlook the value proposition AnalytixLabs offers when it comes to Business Analytics certification.
Following are the 2 most popular courses which caters to business analytics career track:
• Analytics Edge: Main focus of this course is to help beginners build basic to intermediate level analytics skills for descriptive and statistical analysis. This course covers most widely used tools for BI/ descriptive analytics, like MS-Excel, SQL, Tableau and VBA. A major focus on this program is data science and predictive modelling skills using R.
• Business Analytics 360: One of our most coveted course with dual specialization, with combination of Analytics Edge + Data Science & Machine Learning using Python. This is a very extensive 7 month program that covers basis to advanced analytics including Data Science and Machine Learning using Python & R.
All of our training courses are available in following 3 formats:
• Bootcamp/ Classroom contact: Offline classroom sessions with in-person training and mentor interactions and in-class practice.
• Interactive live online: Interactive live online format, which offers real-time interactive experience through in-built VoIP and chat with convenience of attending the class from anywhere globally.
• Self-paced e-learning: Set of pre-recorded video lectures, with flexibility of completing course at your own pace. Candidates can periodically schedule doubt sessions in-person or remotely as per their convenience too.
For all the three formats candidates get an access to online learning portal, to access study material and class recordings for future reference.
Analytics job roles usually demand combination of skills and same is true when it comes to tools. To deliver a project effectively, one may need combination of different tools. We can categorize the most popular tools as following:
• MIS Reporting and Descriptive Analytics – MS-Excel, Tableau, QlikView, Power BI and SQL
• Advanced Analytics/ Statistical Modelling/ Machine Learning – SAS, R and Python
Over last few years new tools like Tableau, QlikView, Power BI with particular focus on data visualization have become very prominent, whereas incumbents like SAS have lost ground to open source tools like R & Python when it comes to advanced analytics.
A comprehensive Business Analytics certification program like Business Analytics 360, covers following keys aspects as part of the curriculum:
Analytics Methodologies: Develop conceptual understanding and learn practical application of various analytical methods, interpretation of different steps involved in end-to-end analytics projects, such as data handling, data extraction, descriptive & predictive analytics using statistical modelling and machine learning techniques.
Analytics Tools: Build hands-on skills on most widely used tools like Excel, SQL, Tableau/ Power BI, R and Python to enable candidates to work effectively on real world problems and be job ready.
Business problem solving: No tools and techniques are of use unless they don’t provide any real business value. An accomplished analyst must know how to define, structure and approach a given problem. With help of multiple case studies and projects spread across the course curriculum, candidates learn to effectively leverage combination of different tools and techniques to answer critical business questions and solve different type of problems.
Some of the common designations offered to business analytics professional are:- Business Analyst, Data Analyst, Analyst, Analytics consultant, Analytics specialist. This depends on the organizational structure and candidates experience and education background. However, in the present world corporate structures are dynamic and designations can vary from organization to organization. One should mainly understand the nature of work and what type of responsibilities a given job role entails.
For a Business Analytics professional work primarily revolves around data, which could be gathered from varied resources, like market research, operations, internal CRM, ERP and BI systems. Business Analytics comprises of deciphering the past trends and/ or make predictions with combination of different analytics tools and methodologies. This ensures the right and most effective action is undertaken. The execution of these occurs by combining statistical software findings with business know-how while trying to reach ideal business solutions. These solutions are shared with stakeholders who utilise this information to make operational and strategic decisions. A skilled Business Analytics professional gauges the quality of data, understand business objective, utilises relevant techniques to generate superior data-driven business decisions.
An experienced analytics professional is also expected to develop domain expertise over a period of time with deep business understanding of at least one industry, for example Retail, FMCG, e-Commerce, Insurance, Banking, Finance, Travel and Logistics etc.
Business Analytics provides a golden opportunity for young professionals to switch to the strategic side of the business. This is certainly a lucrative and sought after job, which involves a lot of hard work to internal and external client expectations.
Minimum qualification to pursue Business Analyst career track is graduation and for Analytics it helps to have graduate degree in quantitative streams, like engineering, eaths, statistics, econometrics, operation research.
After graduation, one has choice of pursuing career in Business Analytics through a certification course. However, for some high paying jobs, which involves consulting and advisory roles, companies prefer candidates with Masters in Management (MBA) or other quantitative fields . Although this degree doesn’t essentially gets you in the field of Business Analytics, but it propels the career faster from base position with higher salary.
Although it not a prerequisite, but good communication skill is important for a Business Analytics professional. As part of the work, a Business Analyst needs to understand the problem and ask the right questions to deliver the project effectively. As part of day to day responsibilities, Business analysts interacts with the client often, hence, it is important put forward different business and technical aspects articulately.
Taking my decision to enroll for the course of Data Science in SAS & R of AnalytixLabs was best decision for me. With the kind of knowledge provided by them helped me a lot during my journey to next phase to job next in Analytics. The thing makes ahead AnalytixLabs is their continual support and helpful and knowledgeable team. Thanks for your support again!
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