Data Science: Basic Course
Data Science- Basic
By obtaining the AEC Basic Data Science certification, you can initiate your journey in the field of Data Science. The certification exam assesses your proficiency in performing data analysis, data visualization, and data modeling, as well as your ability to learn and apply various concepts utilized by Data Scientists.
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About the Certification Exam
The AEC certification exam for Data Science- Basic comprises 60 questions and has a duration of 2 hours. It is an open-book exam featuring multiple-choice and true/false questions that assess knowledge across the major sections of Data Science- Basic. The exam is graded out of 60 marks and is available for testing at more than 1,000 centers worldwide.
Requirements for the Exam
There are no prerequisites required for AEC Data Science- Basic Certification Exam.
Requirements to pass the Exam
To successfully pass the AEC exam, candidates must obtain a minimum score of 40%.
What is the fee for the certification exam?
The exam registration fee is 100 USD.
How to prepare for the Exam?
Individuals aspiring to take the Data Science- Basic certification exam can prepare with Self Learning programs, which serves as a guide for the exam. Additionally, they can enroll in the Data Science- Basic training program provided by AEC Accredited Trainers and Partners to enhance their exam readiness.
AEC Syllabus
- Installation – Anaconda, Pycharm, Virtualenv
- Introduction to python
- Basic Syntax, comments, Variables
- Data Types, Numbers, Casting, Strings, Booleans
- Operators, Lists, Tuples, Sets, Dictionaries
- If…Else, While Loops, For Loops
- Functions, Lambda, Arrays
- Arrays, Classes/Objects, Inheritance, Iterators
- Scope, Modules, Dates, Math, JSON
- PIP, Try…Except, User InputP, String Formatting
- File Handling, Read Files, Write/Create Files, Delete Files
- Ndarray, Data types, Array Attributes, Indexing and Slicing
- Array manipulation, Binary operator, String Function
- Arithmetic, Statistical, Matrix, linear algebra, sort, search, countings
- Data manipulation, Viewing, selection, grouping, merging, joining, concatenation
- Working with text data, visualization, CSV, XLSX, SQL data puling, operations
- Statistics, Linear algebra, models, special functions, optimization
- Probability & Stats Applications
- Basic Probability, Random experiments, Conditional Probability, Independent Events,
- Bayes theorem, Permutation, combination
- Random variable , Discrete/Continous RV, PDF, PMF, CDF
- Joint Probability Distribution, Conversion techniques, EV, varience, SD
- Covariance, Correlation, Chebyshev Inequality, Law of Large number
- Central limit Theorem, Percent & Quantiles, Moments
- Skewness & Kurtosis, Gaussian, Binomial, Standard Normal, Distribution
- Poisson, Multinomial, Hypergeometric, Uniform, Exponential Distribution
- [Mean, median, mode ](Sample/population), Expected values, Variance, standard deviation
- Sampling distribution, Frequency distribution, Estimation Theory
- confidence interval, Maximum Likelihood Estimation
- Hypothesis Testing – Chi-Square, Student’s T, F Distribution, Z test
- Hypothesis Testing – Type-I, Type- II, p Values, Relationship between NULL & Alternative
- Least Square Methods – Numerical
- Data Cleaning – Handling Missing Values(Data Imputation), Dealing with Noisy data(Binning Technique)
- Advance Data cleaning – Will be referred while Regression, clustering topics
- Data Transformation Techniques- Normalization (minmax, log transform, z-score transform etc.), Attribute Selection, Discretization,Concept Hierarchy Generation
- Data Reduction: Data Cube Aggregation, Numerosity Reduction, Dimensionality Reduction
- Data Mapping, Charts, Glyphs, Parallel Coordinates, Stacked Graphs
- Bar, Pie, Line Charts, bubbles, geo maps. Gauge, whisker charts, Heatmaps, scatterplots, plottings images, videos, motion charts, performing EDA
- Building Dashboard – Live implementation – PowerBI
- Implementation of Numerical intuitions
- Regression basics: Relationship between attributes using Covariance and Correlation
- Relationship between multiple variables: Regression (Linear, Multivariate) in prediction.
- Residual Analysis: Identifying significant features, feature reduction using AIC, multi-collinearity
- Polynomial Regression
- Regularization methods
- Lasso, Ridge and Elastic nets
- Categorical Variables in Regression
- Logit function and interpretation
- Types of error measures (ROCR)
- Logistic Regression in classification
- Distance measures – euclidean distance
- Different clustering methods (Distance, Density, Hierarchical)
- Iterative distance-based clustering;
- Dealing with continuous, categorical values in K-Means
- Constructing a hierarchical cluster
- K-nearest neighbours, K-Medoids, k-Mode and density-based clustering
- BIRCH, DBSCAN, Mean Shift, Spectral Clustering, Gaussian Mixture Model
- The applications of Association Rule Mining: Market Basket, Recommendation Engines, etc.
- A mathematical model for association analysis; Large item sets; Association Rules
- Apriori: Constructs large item sets with mini sup by iterations; Analysis discovered association rules;
- Application examples; Association analysis vs. classification
- FP-trees
- PageRank
FAQ's
Data science is the process to draw information from raw data and interpret it into useful insights for business decisions. Data Scientist, Data Analysts, Statistician, Data Engineer are a few of the common job profiles in Data Science. Data science involves a life cycle; capture, maintain, process, analyse and communicate data for business decisions.
Data Science is a comparatively new field with more jobs to offer than the existing fields in computer science and IT. Data Science is a vast multi-disciplinary field with scope of working in leading industries like healthcare, telecommunication, cyber security, finance and others. Data Science has grown with advancement in technology and has more scope of growth in future, offering unaccountable jobs in top MNCs and in top cities.
Any professional belonging to IT, marketing, engineering or software can take a data science course to pursue a career in the fields of data science. Undergraduate students, with more than 50% marks in mathematics, statistics or computer science in 12th examination from science stream are eligible. Graduates with a bachelor’s degree in science, engineering, technology or mathematics are also eligible.. Graduates in business studies like BBA or MBA are also eligible. Data science requires knowledge of mathematics, computer science and statistics.
Data Science certification enables you to start or elevate a career in the fields of data science. Some benefits are:
- Enhanced skill sets to work on different domains.
- Opportunity to work in leading industries.
- Flexibility to switch domains.
- More job opportunities to choose from.
- Higher salaries offered.
- Infinite job opportunities due to high demand.
According to an article published in naukri.com, 3,00,000 plus data scientists would be required in different sectors by 2024, with 3400 positions increasing every month. Common job profiles are:
- Data Scientist
- Python Programmer
- Machine Learning Engineer
- Data Analyst
- Data Engineer
- Statistician
Data Science course with Anexas focuses on training individuals on understanding of Data Science and its aspects, tools and techniques required and skill sets required. The course prepares students for job opportunities with many assignments and real-time projects. Key learnings after completion of this course:
Basic Course in data science: Data analysis, basic visualisation and data modelling.
Intermediate Course in data science: SQL, NLP and different statistical NLP techniques.
Advanced Course in data science: Neural Networks using TensorFlow and Keras, CNN and its different parts.
Yes. The course cost includes the cost of examination, certification, tools, software study material etc. There are no other costs payable once you pay for the course.
Anexas offers the following payment methods:
- Net Banking.
- Card Payment.
- Cash payment.
Cancellation is available 72 hours before the start of the course with 10% deduction. Any cancellation after that is non refundable.
However, Anexas supports custom batches or changes in time and date according to individual preferences, without any additional cost.
Anexas certification course includes all industry level requirements to work in the fields of Data Science. Including tools, softwares, skills and concepts used in different industries. The course opens you to opportunities available in different domains, with job assistance, project guidance, assignments and resume building.
Certification
Candidates who pass the exam will receive AEC Data Science- Basic Training Certificate with lifetime validity. The certificate does not require any renewal. It will be issued in the form of a softcopy* (PDF), which includes a Certificate Code, a Verification Link, as well as the date and time of certification issuance.
*If a hardcopy certification is desired, shipping charges will be applied.