Advanced Certificate in Predictive Analytics for Retail Profit
-- ViewingNowThe Advanced Certificate in Predictive Analytics for Retail Profit is a comprehensive course designed to equip learners with essential skills in predictive analytics, a high-demand field that uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. With the retail industry rapidly evolving and becoming more data-driven, this course is increasingly important as it provides learners with the ability to leverage data and analytics to drive profitability, improve customer experiences, and make informed business decisions.
2,249+
Students enrolled
GBP £ 140
GBP £ 202
Save 44% with our special offer
ě´ ęłźě ě ëí´
100% ě¨ëźě¸
ě´ëěë íěľ
ęłľě ę°ëĽí ě¸ěŚě
LinkedIn íëĄíě ěśę°
ěëŁęšě§ 2ę°ě
죟 2-3ěę°
ě¸ě ë ěě
ë기 ę¸°ę° ěě
ęłźě ě¸ëśěŹí
⢠Data Mining Techniques: Exploration of data mining methods and algorithms used in predictive analytics for retail profit. This unit will cover association rule mining, clustering, classification, and regression techniques.
⢠Predictive Modeling: Introduction to predictive modeling concepts and techniques, including modeling assumptions, overfitting, underfitting, and model validation. This unit will cover various predictive modeling techniques, such as decision trees, random forests, and neural networks.
⢠Time Series Analysis: Study of time series analysis and forecasting techniques, including exponential smoothing, autoregressive integrated moving average (ARIMA), and state-space models. This unit will cover seasonality, trend, and cyclical patterns in time series data and their impact on predictive analytics.
⢠Retail Profit Optimization: Examination of profit optimization strategies for retail businesses using predictive analytics. This unit will cover pricing optimization, inventory management, demand forecasting, and customer lifetime value (CLV) modeling.
⢠Big Data Analytics: Overview of big data analytics technologies and tools, including Hadoop, Spark, and NoSQL databases. This unit will cover data preprocessing, data cleaning, and feature engineering techniques for big data analytics.
⢠Machine Learning for Retail: Study of machine learning techniques and algorithms for retail applications, including customer segmentation, product recommendation, and fraud detection. This unit will cover supervised and unsupervised learning techniques, as well as deep learning methods.
⢠Experimental Design and Causal Inference: Introduction to experimental design and causal inference for predictive analytics in retail. This unit will cover randomized experiments, regression discontinuity designs, and instrumental variables approaches for causal inference.
⢠Ethics and Privacy in Predictive Analytics: Study of ethical and privacy considerations in predictive analytics for retail businesses. This unit will cover data privacy regulations, ethical guidelines for data use, and consumer privacy concerns in predictive analytics.
ę˛˝ë Ľ 경ëĄ
ě í ěęą´
- 죟ě ě ëí 기본 ě´í´
- ěě´ ě¸ě´ ëĽěë
- ěť´í¨í° ë° ě¸í°ëˇ ě ꡟ
- 기본 ěť´í¨í° 기ě
- ęłźě ěëŁě ëí íě
ěŹě ęłľě ěę˛Šě´ íěíě§ ěěľëë¤. ě ꡟěąě ěí´ ě¤ęłë ęłźě .
ęłźě ěí
ě´ ęłźě ě ę˛˝ë Ľ ę°ë°ě ěí ě¤ěŠě ě¸ ě§ěęłź 기ě ě ě ęłľíŠëë¤. ꡸ę˛ě:
- ě¸ě ë°ě 기ę´ě ěí´ ě¸ěŚëě§ ěě
- ęśíě´ ěë 기ę´ě ěí´ ęˇě ëě§ ěě
- ęłľě ě겊ě ëł´ěě
ęłźě ě ěąęłľě ěźëĄ ěëŁí늴 ěëŁ ě¸ěŚě뼟 ë°ę˛ ëŠëë¤.
ě ěŹëë¤ě´ ę˛˝ë Ľě ěí´ ě°ëŚŹëĽź ě ííëę°
댏롰 ëĄëŠ ě¤...
ě죟 돝ë ě§ëŹ¸
ě˝ě¤ ěę°ëŁ
- 죟 3-4ěę°
- 쥰기 ě¸ěŚě ë°°ěĄ
- ę°ë°Ší ëąëĄ - ě¸ě ë ě§ ěě
- 죟 2-3ěę°
- ě 기 ě¸ěŚě ë°°ěĄ
- ę°ë°Ší ëąëĄ - ě¸ě ë ě§ ěě
- ě 체 ě˝ě¤ ě ꡟ
- ëě§í¸ ě¸ěŚě
- ě˝ě¤ ěëŁ
ęłźě ě ëł´ ë°ę¸°
íěŹëĄ ě§ëś
ě´ ęłźě ě ëšěŠě ě§ëśí기 ěí´ íěŹëĽź ěí ě˛ęľŹě뼟 ěě˛íě¸ě.
ě˛ęľŹěëĄ ę˛°ě ę˛˝ë Ľ ě¸ěŚě íë
